logistic regression

The objective of this research is to investigate the factors that enhance the attractiveness of tourism destinations in Tehran. This study is applied in nature and employs a descriptive-analytical methodology. The data collection methods include the use of questionnaires and interviews. The statistical population comprises tourism experts and specialists, and through the application of Cochran's formula and simple random sampling, a sample of 210 tourism experts was selected. Data analysis was performed utilizing descriptive statistics and logistic regression tests.  The findings of the research reveal that, out of the 210 tourism professionals in Tehran, 91 individuals, representing 43.3%, perceived the attractiveness of Tehran's tourism destinations to be at a high level; 29% assessed it as moderate; and only 27% regarded it as low. Furthermore, the results concerning the factors influencing the enhancement of attractiveness in alignment with tourism development indicate that, among the four factors considered, the most significant were: 1) innovative business opportunities, with an impact coefficient of 0.613; 2) the city's natural, cultural, and historical assets, with an impact coefficient of 0.577; 3) the development of tourism infrastructure, with an impact coefficient of 0.497; and 4) urban development, with an impact coefficient of 0.473.

Considering that there are different forms of mass movements in the ChamGardlan watershed, especially along communication routes, agricultural and residential areas, it is necessary to create refugee maps Therefore, it is impossible to examine the factors influencing its situation in order to prevent and control this phenomenon. Therefore, during the field visits, geographical, physiography, land use, vegetation cover, soil, climatology, geography and geomorphology maps were produced in the GIS environment. The method of this research has been accomplished base of distinction of the geomorphological units while using aerial photos and crossing  basis maps. Then, the effective factors on the occuration of mass movements were studied using logistic regression equations. So that, the factors such as slope, geological formation type, pedologic, climatic, etc. were taked into consideration as independant variables, and mass movements occurance frequency as function of mentioned factors.The result indicated that the effective factors related to frequency of land sliding happening in the area in arrangement, are slope, geological formation type and mass  movements  type (both kind and amount of salts in soil) and also landuse.

In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including elevation, slope, aspect, Melton ruggedness number, terrain convexity, stream length, valley depth, topographic wetness index, precipitation, geological formations, distance from rivers, distance from roads, and vegetation cover were utilized as independent variables. Approximately 70% of the watershed's landslide pixels were used for model training, and 30% for model validation. Model validation was performed using ROC curves. The results indicated the higher performance and accuracy of the radial basis function (RBF) kernel of the SVM model for generating landslide hazard maps in the study area. The area under the curve (AUC) for the RBF kernel was approximately 0.951 for model training and 0.944 for model testing. The results suggest that slope with a coefficient of 0.28, precipitation with a coefficient of 0.27, lithology with a coefficient of 0.26, and elevation with a coefficient of 0.22 are the main controlling factors for landslides occurrence in the Zamkan Watershed. Both the SVM model and logistic regression confirmed the deterministic effects of selected factors on landslides. About 35% of the study area as classified as highly susceptible to landslides, primarily in the eastern half of the watershed. Factors such as high elevation, steep slopes, heavy precipitation, and the Kazhdomi Formation's composition were identified as key contributors to this susceptibility.

Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of ​​1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.

Landslides are among the most destructive natural phenomena, and their harmful effects have also increased as a result of increased human occupation and activities in nature. When the resistance force is less than the force of the material weight on the slope due to the sheer force of the soil, landslides would occur with the least intervention of secondary factors including heavy rain, earthquake, etc. Due to the special conditions of geology, climate, tectonics, etc., many landslides occurred in Ghahremanlou catchment area located in North Khorasan province, resulted in a lot of damage to the farmlands, communication lines and rural settlements.The study aimed to identify landslide-prone slopes and classify landslide types in Ghahremanlou catchment using a logistic regression model and fuzzy logic to investigate and identify the factors affecting landslides in this catchment and to draw a landslide sensitivity zoning map. Landslide mapping and identification map can be used as an efficient method in planning to reduce the risks and damages caused by landslides.Ghahremanlou catchment area is located in the southwest of Farooj city, located between longitudes 57 degrees and 55 minutes to 58 degrees and 4 minutes east and between latitudes 37 degrees and 2 minutes to 37 degrees and 13 minutes.The villages of Mayvan, Chokanlu, Ostad, Khosravieh, Hasht Sorkh, and Ghahremanlou are located within this catchment.The area is located between the geological formations of Kopehdagh in the north and Aladagh in the south and has Sandstone, granite and shale rocks. The slopes are composed of Orbitolina limestones from the Cretaceous geological period (with the Tirgan Formation) and red marls and Neogene sandstones. In this study, landslides in the area were determined by field surveys and Google Earth images. Then, using a digital model of a 30 m altitude SRTM sensor, slope map, slope direction, topography, and hydrographic network of the catchment were calculated.A road network map has been prepared from OSM and Google Earth data by digitizing the main road lines and side roads in the study area. The average annual rainfall map is extracted from meteorological data of North Khorasan province and its map is prepared based on the IDW interpolation model. To determine the vegetation of the region, the normalized vegetation difference index algorithm was used. Using the Wetness index, from the Landsat 8 satellite image for 1399, the OLI sensor of the humidity index of Ghahramanloo catchment has been calculated.Then, a fuzzy model and logistic regression were used to determine the effect of each of these parameters and effective criteria in landslides.In this study, 28 slopes in which landslides occurred were identified. Then 12 independent variables have been calculated including slope, slope direction, etc. After preparation using the fuzzy logic model (linear normalization functions (straight and inverse), norm and Gaussian between zero and one) entered the logistic regression model. Then the position of landslides in the region as a dependent variable, zero or one (boolean) was prepared and entered into the logistic regression model. After introducing independent variables related to the logistic regression model, 70% of the pixels with a landslide (in 28 identified positions) were introduced to the model as a training sample and 30% of it as a model for checking the accuracy of the model in the Pseudo index. R2 and ROC were used. The results are obtained by identifying areas with landslide potential using the Natural Breaks function and considering the same variance and scatter of data in each class. These results show that the most effective causes of landslides in the region are distance from the waterway, land use, rainfall, and distance from the road.On the other hand, the farther they are from the communication lines, the lower the impact on landslides. Also, in the land-use variable (according to how it is normalized), barren lands have less effect than rangelands, and also increasing rainfall has a positive effect on the occurrence of landslides in the catchment. In a way that with increasing rainfall, the occurrence of landslides also increases. To evaluate the accuracy of the regression model in this research, the value of Pseudo R2 equal to 0.2311 and ROC equal to 0.9134 indicates a good fit of logistic regression and its appropriate descriptive capability. Based on the landslide classification models by Kruden and Warrens, 1996, the prediction of landslides is classified using a logistic regression model, and landslides in the region into rotational, shallow, and transitional types.The results of the existing landslides indicates that out of the total area of 10241 hectares in Ghahremanlou catchment, 310 hectares, i.e., 3%, and 216.3 hectares, i.e., 2.11%, have very high landslide potential, respectively. And 950.50 hectares of the area, i.e., 9.28%, has a medium landslide potential. On the other hand, 66.5% of the catchment is located in the western direction (west, northwest, and southwest) due to its location at high altitudes and steep slopes, with very little potential for landslides.

Soil erosion by water is one of the most important processes of land degradation, especially in semi-arid regions. Among the different types of water erosion, gully erosion is one of the most important events affecting soil destruction, changing the landscape and water resources, and land regression [1]. Gully erosion is the most obvious form of soil erosion, which leads to a decrease in soil production capacity and restrictions on land use, and can be a serious danger to roads, fences, and various structures, and also causes significant soil losses and the production of large amounts of sediment [2]. This erosion is also called gully erosion. A gully is a relatively permanent waterway that temporary streams of water pass through during rainfall and carry a large amount of sediment [11]. The formation of gullies is always accompanied by erosion and changes in the appearance of the land and causes the production of a significant amount of sediment, destruction of lands, roads, irrigation networks and filling of dams [9]. Gullies, which are considered major indicators of environmental changes in most cases, are not considered normal forms of erosion due to their rapid growth [8]. In the studies conducted both inside the country and abroad, various methods have been used to evaluate the potential of gully erosion, which are mentioned below. Among the methods used to determine the potential of gully erosion are regression models [17, 10, 4, 15, 23, 22], knowledge-based model of hierarchical analysis [6, 21, 22, 3, 29], fuzzy logic. [12 and 13], Dempster-Shafer model [14], artificial neural network and support vector machine [33], etc. pointed out that, based on this, the watershed of Klocheh Bijar in Kurdistan province has been severely affected by this type of erosion and caused the loss of many agricultural lands in the studied basin have been eroded. Therefore, the gully erosion susceptibility mapping of the studied basin was studied and investigated using Logistic Regression and Fuzzy Logic models, and finally mapping of gully erosion in the studied area and the validity of both used models were verified.

Gully erosion inventory map In this study, 950 points at the top of gullies (head cuts) were recorded as a distribution map of gully erosion using Google Earth images and field survey. Then, they were divided into two parts of training data (70%) and validation (30 percent) were divided. The training data were used in the model learning section with the logistic regression method and the validation data were used to determine the validity/prediction power of the models. Logistic regression model Multiple logistic regression is a multivariate technique that considers several physical parameters that may affect the probability. In this method, the values of the independent variable can be expressed in binary form (0 and 1) and as a numerical quantity. Fuzzy logic model In classical set theory, an element is either a member of the set or not (zero and one). Fuzzy set theory extends this concept and introduces graded membership. So that an element can be a member of a set to some degree and not completely. In other words, a fuzzy set is a set of elements with similar characteristics, where the set has a certain degree from zero to one. Zero means no membership and one mean full membership [24]; Therefore, before implementing the fuzzy model, it is necessary to determine the membership functions for each of the layers mentioned above and set the value of the layers in a range between (zero and one) and then enter the layers into the fuzzy model. To implement the fuzzy technique, a gamma operator is needed, the value of the modulating gamma is between zero and one, zero gamma is equivalent to fuzzy multiplication and one gamma is equivalent to fuzzy addition.

Gully erosion susceptibility map logistic regression Table. 1 shows TOL and VIF values of factors affecting the occurrence of gully erosion. Viewing this table shows that all effective factors have a TOL value greater than 0.1 and a VIF less than 10, which indicates the absence of multiple collinearities between them, and all of them are used as input for the model. They were branched with appropriate logistic regression. Fuzzy Logic Figure 5 shows the prediction map of gully erosion using fuzzy logic. Although the pattern of distribution of different areas from the map obtained with the fuzzy logic model follows the logistic regression model, but it seems that an exponential exaggeration in the area of the area with the probability of gully erosion is very high is seen, which is probably related to choose the numbers for the gamma value during modeling with this method. However, the areas with high and very high probability of occurrence correspond to the top of gullies (head cuts).

Gully erosion is one of the most important natural hazards and the main cause of land degradation in the Klocheh Bijar watershed in Kurdistan province. Recognizing the most important factors affecting the occurrence of this phenomenon as well as predicting areas prone to its occurrence is one of the management and preventive measures to better understand the area before any engineering/structural and biological measures (land management) or a combination of both. Reduction of possible damages. Preparing a gully erosion susceptibility map can be a useful guide for planners, managers, organizations and decision makers regarding the management of these areas. In this research, 950 points at the top of gullies (head cuts) were recorded as a distribution map of gully erosion using Google Earth images and field survey and were divided in the ratio of 70 to 30. Multilinear correlation test was used to check the internal correlation of 20 effective factors, as well as two models of logistic regression and fuzzy logic were used to prepare prediction maps of gully erosion in the study area.

Slope instabilities are one of the most distinctive types of geomorphic hazards that are exacerbated by human interference and threaten most of the human installations, especially mountainous highways and impose heavy costs on the government and local residents. Each year, slope instabilities cause enormous economic damages to highway, railways, power transmission and communication lines, irrigation and watering canals, ore extraction, as well as oil and gas refining installations, infrastructures in cities, factories and industrial centers, dams, artificial and natural lakes, forests, pastures and natural resources, farms, residential areas and villages or threaten them. Nowadays, many instabilities are resulted by human intervention and manipulations. One of the effective human factors in instability occurrence is the construction of highway. Highway construction, especially in mountainous areas, increases the probability of occurrence of various types of instabilities, as it changes the natural balance of the slopes and causes deformations in the land. Each year, lots of casualties and financial losses are imposed by the occurrence of various types of instabilities in the slopes overlooking the highways, which also cause the destruction of many natural resources in the country. However, the construction of roads, highways and freeways is necessary and unavoidable in today’s life.The Tehran-North highway will be the route that connect the Iran’s capital Tehran with the southern shores of the Caspian Sea.

This contribution aims to study slope instabilities along this highway using logistic regression method. In this regard, layers of 14 effective factors were identified, comprised of elevation classes, slope, aspect, geology, land use, precipitation, distance from fault, river and highway, normalized difference vegetation index (NDVI), climate, slope length (LS), stream power index (SPI) and topographic wetness index (TWI). Consequently, maps of the factors responsible for instabilities were prepared as separate layers in the GIS environment and transferred into the Idrisi software. The whole procedure included: (1) preparation of digital elevation model (DEM), river and fault layers based on the 1:25,000 topographic map of the area, as well as distance maps from rivers and faults, (2) creating slope and aspect maps from DEM, (3) preparation of land use and NDVI maps of the region based on unmatched classification of Landsat 8 image of OLI sensor, (4) preparation of geological map, (5) preparation of precipitation and climate layers based on the information obtained from the meteorological organization, (6) creating LS, SPI and TWI layers based on the DEM, (7) conversion of the distribution data of the regional instabilities using Landsat satellite and Google Earth images, (8) correlating the information layers with the regional instability map and calculating their density per unit area, and (9) performing the logistic regression model using Terrset software.

Results obtained by applying logistic regression model showed that the most important factors affecting slope instabilities in the Tehran-Soleghan highway area are distance from fault and climate. 27.14% of the Soleghan highway area possesses medium to high potential for instabilities, within which 86.26% of the instabilities have occurred. Furthermore, 4.57% of the Soleghan highway area shows very high risk in terms of instability occurrence, encompassing 61% of the occurred instabilities. According to the prepared maps, the middle and southern parts and a small section in the north of the Tehran-Soleghan highway area have the highest potential for instability occurrence. The high value of the ROC index and its proximity to the end value of 1 indicates that instabilities strongly correlate with the probability values derived from the logistic regression model. Additionally, the assessment of the instability potential map by the SCAI index showed that there is a high correlation between the prepared risk maps and the occurred instabilities, which have been confirmed by field surveys. The obtained results are in a good agreement with the general opinion that SCAI decreases especially in high and very high risk classes and indicates a high correlation between the prepared risk maps and the occurred instabilities and field surveys in study areas.

Finally, it can be mentioned that the logistic regression model is suitable for preparing the zonation of the probability of instability occurrence along the edges of the studied highway. As a final conclusion, it can be concluded that in addition to natural factors, the- human-made factors and particularly unsystematic highway  construction can play an important role in the instability occurrences on the slopes overlooking the highway  and in order to reduce the relative risks and increase the stability of the slopes, it is necessary to avoid manipulating the ecosystem and changing the current land use as much as possible, in addition to policy making for constructions in accordance with geomorphological and geological features of the area.

Urban development and irregular migration of rural population to urban areas are significant phenomena that have damaged agricultural lands, natural landscapes, and public open spaces. This issue doubles the need for informed guidance and spatial organization to better understand the processes of urban development for future planning. The present study aimed to evaluate the growth of Hamedan city from 1996 to 2019 and then simulate until 2041. The research method is descriptive-analytical, and the cellular automation model was used to simulate physical development, and logistic regression was applied to analyze the impact of different variables on physical growth and the Markov chain was used to analyze user changes. The validity of Landsat satellite images is also evaluated with respect to the kappa value and acceptable overall accuracy. The results indicate that city center and agricultural land variables with ROC of 0.873 and 0.881, respectively, had the most impact on Hamadan urban growth during the last 23 years. The area of urban areas in 1996 was doubled compared to the year 2011, and almost 2.5 times more than in 2019. On the other hand, population growth increased 1.48 times over the past 23 years. This indicates that the growth rate of urban areas exceeded the population growth rate in Hamadan. The results of the model evaluation indicate that the integrated model is able to provide a precise understanding of urban processes and developments such as evaluating past developments and predicting directions and rates of future physical development.

Forests play a vital role in the sustainability of ecosystems as one of the most important natural renewable resources. One of the most important disturbances affecting Zagros forest ecosystems is forest fires. Therefore, identifying critical fire areas to reduce potential damage is necessary. The purpose of this study is to investigate the effect of effective variables in causing fire and to prepare a fire risk map. For this purpose, the variables affecting the fire including altitude, slope, direction, Distance from residential areas, distance from waterways and distance from the road were determined to determine the impact of each on the fire. Elevation map of sea level, slope and geographical direction was prepared with the help of digital elevation model. Distance maps of residential areas and distance from the road were prepared from digital maps of 1.25000. Also, the areas where fires occurred during the years 90-94 were harvested by GPS. In this study, logistic regression method was used to investigate the effect of various factors on fire. The results showed that altitude, distance from waterway and slope percentage were the most important factors influencing forest fires in the region. Modeling was performed based on three variables that had a significant relationship with forest fires in the region and the coefficients obtained from the logistic regression method. The validation results of the model with a negligence coefficient of about 0.500 and a rock curve coefficient of 0.701 showed the accuracy, fit and validity of the obtained model. The results also showed that 81% of the area is located in critical and dangerous areas.

The effect of Tamarchin frontier market on physical development of Piranshahr over 2006-2014IntroductionCommon frontier markets are considered as the major factors that transform frontier cities and are booming the economy of these regions. Therefore, frontier markets play an essential role in developing the economy, promoting peace and stability, increasing security, and improving infrastructure and services in these regions. Activation of these markets leads to greater economic growth for frontier cities, i.e. a trend that results in economic development and urban growth. There is no doubt that development of economic activities leads to an increase in employment and to a large extent causes population and urbanization growth. On the other hand, there is a direct correlation between increasing growth of urbanization and physical development of cities. In other words, indiscriminate and unsustainable development of cities leads to growth of suburbs, destruction of urban green areas, and rising demand for urban land. Therefore, it can be stated that the economy of a city is an effective factor in growth of urban lands and physical development.Activation of Tamarchin frontier market resulted in a fundamental change in the economy of Piranshahr. And employment and the revenues from Tamarchin boundary and domestic market transformed the economy of the town. Therefore, in recent years high level of employment in Piranshahr has made it one of the population absorbers in the area, a fact indicated by population changes. Due to increasing need of the immigrants to accommodation and shelter and disorganized constructions along with real estate activities, urban development has been pushed toward Northeast, East, South, and Southeast where there is no physical development limit. This physical development has swallowed up the agricultural lands and "Shin Abad" village has been affected by this development and integrated into the town. On the other hand, due to this physical development of the town toward flat and fertile plains, land cover has been changed and agricultural lands have been destroyed.In this regard, the present study is aimed at investigating the effect of Tamarchin frontier market growth on immigration to Piranshahr and its physical development whereby the main hypothesis of the study – development of Tamarching frontier market has caused immigration to Piranshahr and its physical development to increase – can be answered.MethodIn terms the aim, the present study is an applied one and in terms of the method and nature it is descriptive-analytical. According to the title of the study, required data have been collected through library and documentation procedures, survey techniques, and satellite images and were analyzed through bi-variate logistic regression and Holden models and SPSS. The procedures are as follow:Survey techniques are utilized in order to evaluate opinion of Piranshahr residents on the effect of Tamarchin frontier market on physical development of the town. The sample size has been determined through Cochran formula to be 384 individuals. Collected data have been analyzed through bi-variate logistic regression and Holdern models using SPSS. Moreover, in order to identify the pattern of physical development of the town, periodical land cover maps retrieved from Landsat satellite, satellite images of ETM+TM (2006 and 2014) were analyzed using IDRISI software. In addition, Holdern model has been used in order to explain the issue more and identify this issue that development of Piranshahr has been due to population increase or its density pattern.DiscussionDiscussion of the study findings begins with a short review of population changes of Piranshahr and the performance of Tamarchin frontier market so that an overview of the market is achieved. Then, in order to determine the effects of the development of Tamarchin frontier market on population absorption of the town, collected data retrieved from Piranshahr residents have been utilized to see whether the market development has affected the population absorption and physical development of the town or not. In so doing, bi-variate logistic regression model in SPSS has been applied.In the next phase, the pattern of physical development of the town has been identified through satellite images and Holdern urban development model. This model has also been utilized to determine the proportion of the town development and its population.ConclusionBy developing the economy of frontier cities, frontier markets create a high level of employment in these regions, which results in a large population to be absorbed in order to find appropriate jobs and gain higher income. It is vivid that as population absorption increases, demand for urban land rises. Therefore, increasing growth of urbanization is directly correlated with physical development of cities. In other words, indiscriminate and unsustainable development of cities leads to growth of suburbs, encroachment on the lands around the city, destruction of urban green areas, and rising demand for urban land. Therefore, it can be stated that the economy of a city and its growth are effective factors in land and physical development of the city.In this regard, the present study is aimed at investigating the effect of Tamarchin frontier market growth on immigration to Piranshahr and its physical development. The results of integrating data collected from surveying and logistic regression model in SPSS have presented important information on the pattern and trend of land cover change and proved the fact that Tamarchin frontier market and the resulted economic growth in recent years have created a high level of employment which is an essential factor in immigration to Piranshahr and in fact "its physical development". This rapid urban development has cause Shin Abad village to get integrated in Piranshahr. Moreover, the results gained from Holdern model, further explaining the issue, have indicated that development of Piranshahr is mostly caused by population increase (- 0.997) rather than by the patterns of urban development. Although Shin Abad has integrated into Piranshahr and 190 Acres has been added to the town during 2005-2014, 118 Acres of this area (62% of the total area added to the town) have been unofficial settlement. However, the town's development has not been comprehensive and the town has not experienced spiral development.Keywords: Physical Development, Tamarchin Border Market, Piranshahr city.

Landslides are one of the most important environmental processes, especially in mountainous landscapes. Identifying sensitive areas and preparing a landslide risk zoning map is an important step in preventing and reducing the damage caused by this phenomenon. Hashtjin basin, with its mountainous face and considering the geological, lithological, climatic and human conditions, has the necessary conditions for the formation of landslides; therefore, the current study aims at landslide risk zoning in the given area. Therefore, landslide sensitivity analysis for Hashtjin watershed is evaluated according to the efficiency of the results obtained from two models of logistic regression and Anfis to achieve the research goal. Using the interpretation of aerial photographs and field visits, control areas, as a dependent variable, were recorded by GPS. Then, the factors affecting landslides in the area including slope, direction, elevation lines, distance from waterway, distance from fault, distance from road, geology, land use and rainfall were identified according to various sources, field studies and consultation with experts; Then, layers were prepared as independent variables in GIS Arc environment. Moreover, logistic and ANFIS regression models were implemented by entering the aforesaid layers into TERRSET and MATLAB software environment, respectively. The final landslide hazard map of the area was prepared in 5 hazard classes. In this study, 25% of the control samples were used as test data to measure the accuracy of the studied models. The results of validation of the performance of the mentioned models by performing the ROC curve showed that the accuracy of Anfis model and logistic regression were equal to 88.23 and 86.45%, respectively. The findings from Enfis model reveal that approximately 4854 hectares, equivalent to 20.6% of the Hashtjin area are in high and very high class in terms of landslide risk.

Rivers react to subsidence at their baseline by cutting and digging topographic features. The development of an upstream incision is often accompanied by a steep fracture called a river break (Loget & Van Den Driessche, 2009). The presence of river breaks in a geographical landscape is an indication of a steady-state in river systems. Therefore, the presence of knickpoints shows the system instability. The study of knickpoints can be used in the field of studies related to the evolution of valleys, identification of tectonic active areas and rock outcrops, river surface changes, erosion and sedimentation, and geomorphological changes in river systems. The basin studied in this study is located in the Qaleh Shahrokh-Chelgard area in the northeastern part of Chaharmahal and Bakhtiari province, Iran. The reason for selecting this basin is the extensive activities of the Zagros fault along the northwest-southeast and the existence of a hydrographic network affected by the trend of faults and the potential for knickpoints.  

In this study, the locations of knickpoints were detected from the Radiometrically Terrain-Corrected (RTC) model which is extracted from the active microwave sensor ALOS PALSAR with a spatial resolution of 12 meters (Logan et al., 2014) as input data to the MATLAB executive toolbox called Tec DEM. Tec DEM is an executable toolbox in MATLAB software and uses a Digital Elevation Model (DEM) as input for morphotectonics in the basin. Tec DEM tool can be used in a variety of fields in the analysis of surface anomalies, drainage network and surface dynamics of basins, production of base maps, incisions (local roughness), vertical dissection and drainage density of basins and sub-basins, determination of turning points or knickpoints, hypsometric analysis and slope and concavity index of canal profiles (Shahzad & Gloaguen, 2011). The determination of knickpoints according to the shape of the longitudinal profile of the river is done semi-automatically. In this study, these points in the study areas were investigated according to field observations. In this study, geological variables and geomorphic variables related to knickpoints were used to identify the knickpoints. Information layers including geology, distance from the fault, distance from the boundary of geological formations, surface roughness index, fractal dimension, base surfaces, local roughness, and the vertical dissection as predictor variables and the layers of knickpoints as the prediction variables were used for modeling. For geological and tectonic studies of the region, geological maps of 100,000 sheets of Chadegan and Fereydunshahr and 250,000 sheets of Shahrekord were used. A total of 8 raster layers were used to analyze and predict the possibility of the presence of a knickpoint in the study area. Since 8 layers have different units and are not suitable as direct input for logistic regression, the input parameters were normalized in the range of 0 to 1. Nominal layers, such as geological data, became sequential variables between 0 and 1. All of these layers were then re-sampled as a network format with a cell size of 195*195 m using the nearest neighbor method, to allow all layers to be combined. Then, a matrix of square cell structure was prepared for the study area. It consisted of a matrix of 273 rows and 273 columns representing a total of 39,650 cells. Of these, 74 cells were identified as knickpoint points. These areas were identified with code 1 (presence of knickpoint) and the rest of the cells that did not have knickpoints were recorded with code 0 (absence of knickpoint).  

The probabilistic relationship of the presence of a knickpoint as one of the important results of the research was obtained by the logistic regression method. This relationship predicted the probability of the presence of knickpoints based on geological and geomorphic variables. The probability map of the knickpoints in the study area was obtained based on the statistical relationship. According to the results, there is a possibility of river knickpoints in the southwestern regions and parts of the northeastern Basin. The results of the probability ratio test to determine the statistical significance of each of the independent variables in the proposed model showed that the geology and the distance from the boundaries of the geological formations in the model were significant. The results of the Yuden index for the training dataset, validation, and test data were equal to 0.72, 0.76, and 0.66, respectively, which indicated the accurate information on the probability status of knickpoint points, especially for the test data of the model. The results of the Kappa agreement coefficient for training, validation, and test data were also equal to 0.62, 0.73, and 0.60, respectively, which indicated the agreement of both methods with the observed values ​​of knickpoints.  

The results of this study showed that at the boundary of lithology, because of the presence of joints and cracks due to differences in the type of rocks, the probability of the presence of river break was more than other parts of the region. Although the presence of some relatively high slope knickpoints indicated active tectonics in that area, in the present study, the effect of the fault system or active tectonics in the formation of knickpoints was not statistically significant. Particularly, the reduction of local roughness index and baselines was associated with less tectonic activity, but in this study, the appearance of knickpoints has been associated with a decrease in these two factors.

Paying attention to environmental effects on the production of agricultural yields can be very useful in the direction of sustainable agricultural management. Understanding biological behaviors in the production of pollutants can play an important role in reducing the adverse effects of air pollution. Logistic regression method is considered as a linear developed method to predict air pollution; Time series analysis of parameters affecting air pollutants and addressing how much data is needed in previous times to predict the amount of pollutants one step ahead is an issue that has been less studied. The current study aims to model the process of five important pollutants including carbon monoxide (CO), ozone (O3), particulate matter less than 10 μm in diameter (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) in Mazandaran province, using logistic regression method and time series analysis, to examine the efficiency and flexibility of the methods used in modeling and forecasting these pollutants. In this study, meteorological data from Ramsar, Amol, Babolsar and Nowshahr stations and air pollution data from Gulogah, Ghaemshahr, Sari and Kiasar stations were received daily in the second half of 2017 and 2018, the average of which has been used in data analysis. The findings reveal that NO2 and CO of Gulogah station and O3 of Kiasar station and SO2, NO2 and CO of Sari and Ghaemshahr pollution stations are completely related to the parameters of temperature, relative humidity and wind speed, which indicates the effect of these parameters on changing the concentration of these pollutants. Moreover, based on the patterns of univariate functions of regression equations, valid formulas for estimating logistic relationships between pollutants and meteorological parameters were extracted, according to which, having meteorological parameters in stations, it is easy to predict the pollution of the region.

Pressure on the environment for human activities is important not only environmentally but also economically. In Iran, due to the abundance of energy resources, there is waste and extravagance in their use for economic activities, which leads to an increase in environmental pollution. The current study aims to predict five important pollutants including carbon monoxide (CO), ozone (O3), particulate matter (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2). In fact, this study is going to investigate the process of pollutants using logistic regression method and time series analysis to examine the efficiency and flexibility of these methods in modeling.

Studied air pollution measuring stations in this work include Gulogah, Ghaemshahr, Sari and Kiasar stations. Data from Ramsar, Amol, Babolsar and Nowshahr meteorological stations were also used. Meteorological data from synoptic stations and air pollution data from monitoring stations of the Environmental Protection Organization were received daily in the second half of 1396 and 1397, the average of which was used in data analysis. In statistical analysis, the correlation between the parameters was calculated and the correlation relationships were presented. In this study, logistic regression method was used.

With the obtained values ​​for special vectors suitable for each component, a suitable drawing of the relationship between meteorological parameters and air pollutants is created. The results showed that increasing each parameter has an increasing effect on the output, while decreasing each has a decreasing effect on it. A closer look reveals that the O3 pollutant is directly related to the temperature parameter and inversely related to humidity. Moreover, the findings from the test phase as well as the prediction error in the network test phase and the correlation between the actual data and the predicted data indicate that the coefficient of determination R2 between the actual data and the predicted data is equal to 0.62. The relationship between the actual values ​​of O3 and the error obtained from the network test reveal that there is no systematic relationship between the values ​​of O3 and the error and there are different errors for different values ​​of O3. Performing logistic regression and examining the accuracy obtained from them to predict the other four pollutants, it was concluded that there is no systematic relationship between the values ​​of these pollutants and the error and their different values ​​have different errors. Based on this, the error value for these pollutants in the range of 0.4-0 can be assumed to be approximately in a range from 0.07 to 0.1. Besides, studies show that there is a significant relationship between O3 and temperature in Amol meteorological station and Ghaemshahr pollution station. Furthermore, the correlation between meteorological parameters and pollutants in Ramsar meteorological stations and Kiasar pollution shows a significant relationship between SO2 and wind speed, while it indicates a significant relationship between O3 and temperature for Babolsar meteorological stations and Sari pollution. Moreover, correlation study for Nowshahr meteorological stations and throat pollution show a significant relationship between SO2 content and temperature.Based on the correlation results, there is a positive and significant relationship between O3 and temperature in Amol meteorological stations and Ghaemshahr pollution, as well as Babolsar meteorological stations and Sari pollution. Besides, the study of the correlation between meteorological parameters and pollutants in Ramsar meteorological stations and Kiasar pollution proves a negative and significant relationship between NO2 and temperature, and for Nowshahr meteorological stations and bottleneck pollution, shows a significant negative relationship between SO2 and temperature. Therefore, the results clearly indicate that temperature is the most effective factor in the process of creating pollutants in Mazandaran province. This result is consistent with the results of Khorshiddoost et al. (2015) which investigated the relationship between atmospheric parameters and air pollution in Tabriz. However, it contradicts the results of Mahneh (2015) Taste and Kakhki study, which examined the relationship between climate elements and air pollution fluctuations in Mashhad, in which relative humidity was identified as the most influential factor on CO and SO2 pollutants; On the other hand, it is noteworthy that at different stations, different elements have a significant relationship with temperature; This difference in the performance of spatial models for different stations has been confirmed in other studies.

According to the findings from the studied stations, it can be said that NO2 and CO of Gulogah station and O3 of Kiasar station and SO2, NO2 and CO of Sari and Ghaemshahr pollution stations completely indicate a significant relationship among the parameters of temperature, relative humidity and wind speed. The effect of these parameters is to change the concentration of these pollutants. Given the uniformity of changes in station data, it can be inferred that the resulting changes follow general patterns; thus, the stations that have a higher correlation coefficient have closer and more similar patterns and the stations that have a lower correlation coefficient will have unique and special patterns for the same station. Performing logistic regression and examining the accuracy obtained from them to predict pollutants, it was concluded that there is no systematic relationship between the values ​​of these pollutants and the error and their different values ​​have different errors. Finally, based on the purpose of this study, valid formulas to estimate logistic relationships between pollutants have been extracted in order to investigate the efficiency and flexibility of these methods in modeling the pollutant process using logistic regression and time series analysis, based on univariate models of regression equations of models. According to these equations, it is easy to predict the level of pollution in each region by having the meteorological parameters in the stations.

According to the First World Conference on Deserts and Desertification, desertification refers to the destruction and degradation of natural ecosystems in arid, semi-arid, and sub-humid arid regions, which results in lower biomass production and the emergence of soil erosion (Ekhtesasi et al., 2011). Desertification results from natural factors such as climate variables and anthropogenic activities (Binal et al, 2018; Claado et al, 2002) and its impact on ecological processes is enormous and complex. Therefore, counteracting desertification is necessary to maintain long-term soil fertility in arid areas of the world. The present study aimed at evaluating desertification trends in the areas surrounding Lake Urmia in the period from 2000 to 2018. The main objectives of this study were 1) identification of the most suitable spectral index pair of desertification in the study area during the study period, taking into account the statistical relations; 2) mapping the desertification risk for the study period and the assessment of desertification trend in the study area by using the spectral biophysical indices such as normalized difference vegetation index (NDVI), surface albedo, Tasseled cap along with three components of brightness, Wetness, and greenness, and 3) identifying the most important factor that caused desertification in the study area by using the logistic regression model.

In the present study, first, three frames of Landsat 5 TM sensor and seven frames of Sentinel 2 images were downloaded and analyzed by ENVI5.3 and QGIS software for July 2000 and 2018. In the next step, spectral indices of desertification, including the normalized difference vegetation index (NDVI), surface albedo, Tasseled Cap (including three components of brightness coefficient, Wetness, and greenness) were extracted for the study period. Thereafter, using the statistical relations and the determination coefficient, the most suitable spectral index pair of desertification in the study area was identified. After the identification of suitable spectral index pairs, the selected spectral index pair was normalized and the desertification mapping was performed for the years 2000 and 2018 taking into account the obtained gradient by using the linear regression relation. Finally, by applying the statistical change detection method, changes in the class's risk were investigated and using the Logistic Regression model, the most effective factor in the occurrence of desertification was identified.

Discussion :

The normalized difference vegetation index (NDVI), wetness, and greenness were considered as the independent variables and surface albedo and brightness coefficient as dependent variables. The pairs of NDVI-Albedo spectral indicators have a positive correlation, but two spectral index pairs of humidity-brightness coefficient and brightness coefficient-greenness due to having a negative correlation were selected as the desertification index pairs and then normalized in the next step through the relevant relations. After mapping the desertification risk according to the index pairs of brightness coefficient-greenness and humidity-brightness, the combined map of desertification was obtained using line slope from the normalized relationship of the selected index pair and overlay function for the years 2000 and 2018 in 5 classes of non-desertification, weak, moderate, severe, and relatively severe desertification risks. To verify the results, using the classification algorithm, the Maximum Likelihood Algorithm and the Error Matrix were obtained, and the algorithm, with the accuracy of 91.96 and the kappa coefficient of 0.95 for 2000, and accuracy of 91.25 and a kappa coefficient of 0.89 for 2018 indicated a good correlation between the obtained results and the real-world data. 

The results of this study were as follows: A) The two spectral index pairs of humidity-brightness coefficient and brightness coefficient-greenness were selected as the most suitable desertification indices in the study area, and therefore, the desertification risk maps were obtained through using this spectral index pair, B) The classification algorithm showed the highest degree of similarity with the accuracy of 91.96 and the kappa coefficient of 0.95 for the maps of 2000, and accuracy of 91.25 and a kappa coefficient of 0.89 for the maps of 2018, which indicated a good correlation between the obtained results and the real-world data, C) According to the results of statistical change detection analysis method, the areas of ​​severe, relatively severe, and moderate desertification risk classes were increasing from 2000 to 2018, D) The desertification risk maps of 2000 and 2018 showed that the lands on the eastern coast, and especially on the southeast of the Lake Urmia, and the areas at the marginal edge of Tabriz Plain, overlooking the Lake Urmia were more sensitive to the desertification risk, and showed more severe degradation, compared to those on the west coast of Lake Urmia, F) Indicators such as underground water electric conductivity, chlorine index of underground water, Sodium adsorption ratio, drought index, Percentage of vegetation, had a high impact on the occurrence of desertification.

Mass movements of the earth's surficial materials downward the slopes is called slope instability, which is affected by the earth gravity, while the rate of material mobility increases by the presence of water in the sediments. Each year, slope instabilities cause enormous economic damages to roads, railways, power transmission and communication lines, irrigation and watering canals, ore extraction, as well as oil and gas refining installations, infrastructures in cities, factories and industrial centers, dams, artificial and natural lakes, forests, pastures and natural resources, farms, residential areas and villages or threaten them. Nowadays, many instabilities are resulted by human intervention and manipulations. One of the effective human factors in instability occurrence is the construction of roads. Road construction, especially in mountainous areas, increases the probability of occurrence of various types of instabilities, as it changes the natural balance of the slopes and causes deformations in the land. Each year, lots of casualties and financial losses are imposed by the occurrence of various types of instabilities in the slopes overlooking the roads, which also cause the destruction of many natural resources in the country. However, the construction of roads, highways and freeways is necessary and unavoidable in today’s life. The Karaj-Chaloos road and the Tehran-North highway are two routes that connect the Iran’s capital Tehran with the southern shores of the Caspian Sea, but suffer frequent slope instabilities.

Energy plays a major role in providing welfare of urban and rural households, and reforming energy consumption patterns, in addition to price balancing, requires recognition and acts of cultural and social variables affecting the pattern of consumption and savings. Considering the importance of saving electricity and its relation with consumer behavior, in this study, the difference in urban and rural communities was investigated in terms of effective factors on energy savings. The present research is descriptive-analytical in terms of purpose and method. The data-gathering tool and information collection and interviews with urban and rural households in Poledokhtar city. The statistical population includes urban and rural households in Poledokhtar Township (N= 30012). Using Cochran formula and simple random sampling method, 379 households (244 urban households and 135 rural households) were selected. In the data analysis section, analysis of variance and logistic regression tests were used. The results showed that there is a significant difference between the factors and indicators affecting power saving in rural and urban areas. The individual agent and the factor of behavior management and purchasing, while the factor is the most important factor in saving households in rural areas, primarily influence power saving in urban areas.

Rapid urbanization has put heavy pressure on the land and it is surrounding resources, reducing vegetation, reducing open space and serious social and environmental problems. An important step in managing and planning urban development, as well as assessing its cumulative effects, is to study and simulate the physical development of the city. One of the processes in which a city changes can be investigated for a multi- year period and therefore predicting the directions of urban development for appropriate planning is urban development modeling, so urban designers and planners have spatial information and when they are related to urban growth patterns, they need to do the modeling. The aim of the present study is to model the urban development of Bandar Abbas city using the LCM model in the 21st year series ( 1994, 2002, 2009, 2015) using the  Landsat satellite images. In the first step, after classification of images by supervised method, mostly we compared the maps with the accuracy of the KAPPA coefficient of 0.9550, and then using the land- change model to predict the map of 2021 and using the Logistic regression model and the CA-Markov chain, the development of the city in 2021 was anticipated exactly. After calculating the matrix, the probability of transfer of changes to predict land use in 2009, 2015, and 2021 was calculated using Logistic regression, which compared to land use in the years 2009 and 2015, the accuracy of the Kappa coefficient was 75.3% for 2009 and 86.9%. Also for the year 2015 we achieved relatively good results.

Instability of natural slopes is one of the geological and morphological phenomena that has a significant role in changing the form of surface of the earth, and when it affects human activities, it can become a dangerous phenomenon (Esfandiari, 2006: 113). Landslides as geological events related to the transportation of soil / heavy rock materials and assessment of its sensitivity, is an important task for local authorities to plan and reduce the land (Xialong Deng, 2017: 2). Therefore, many attempts have been made to assess the dangers of mass movements, and it is suggested to have its reduction methods based on the key characteristics of the slip, including scope and extent, volume, startup mechanism and recurrence, and subsequently, make decisions (Kuo Jeong Chank et al., 2018: 700). (Hemati and Hejazi 2017: 24-7) evaluated the landslide hazard zonation of Lavasanat watershed using logistic regression statistical methods and the result was stated in this way that in the studied area, areas with high risk of zoning, had a large share of the area amount of ​​the region.  Aliabad basin with the southwest - northeast trend in the geographical coordinates of -   located in the east and - located in the north latitudes of the northeast of East Azarbaijan Province and southeastern part of Horand County.(Figure1) Figure (1): Geographic location of Aliabad watershed

 Topographic map (1: 50000) and geological map of Kaleybar region (1: 100000). 2- Landsat satellite images of 8 OLI sensors 3- GPS devices 4- Maps of the faults, slopes, isohyet, isotherm, evaporation, land use, elevation and hydrology 5- Envi 5.3 software 6- Statistical software of SPSS, version 16. For zoning the risk of rock falls, nine layers of information including slope, hypsometry of the region, isohyet, isotherm, evaporation, distance from the fault, distance from the river, land use and lithology were used as independent variables and to prepare the layers in Arc GIS, 1,500,000 topographies and 1.100000 geology maps were utilized, and Landsat 8 satellite imageries were used with the OLI sensor to produce the land use layer. So, after preparing the considered data, the layers were classified as raster, and in their descriptive table, a column called the standard weight was added and the classes related to each layer were calculated using a sum ranking method. In this research, the rock fall layer was considered as the dependent variable and the 9 presented layers were considered as independent variables and all layers had been evaluated in the normalization of the weight between zero and one per pixel; based on the proportion table method, each layer, having 500 weighted pixels that overall included 5000 pixels, was entered into the SPSS environment and regression analysis was performed thereof. Independent variables, including 9 variables, consisting of three PhDs in geomorphology and two Phd in geology were selected based on exports opinions considering their importance in creating and strengthening the dependent variable were weighted between zero and one numbers.

The Chi square test for each of the independent variables, separately, showed that there was a significant relationship between the independent variables and the dependent variable, and the effects of these variables on the dependent variable was acceptable. The numerical value of R was 0.953, and if the R value was closer to one, it would indicate the high validity of the test. The numerical value of the coefficient of determination of the independent variables relative to the dependent variable was 0.909, which indicated the high validity of the significance of the test, because it was closer to number one. Of course, it is clear that the value of the determination coefficient in Pseudo R Square was determined to be good, so the adjusted coefficient of determination was considered whose numerical value was 0.907. These findings indicated that roughly 90 percent of rock falls occurred in the Aliabad basin have been affected by these 9 estimated independent variables. Given that the statistical analyzes confirmed the validity of the effects of independent variables on the dependent variable according to the weightings of the experts in terms of zero and one for each variable as well as the importance of the variables in relation to each other as a binary comparison, the zoning of the risk of rock fall for the Aliabad watershed of the Horand basin was done using Arc Gis software, and in this zonation, five falling risk classes were used including very high, high, medium, low and very low .

lithology and the distance from the fault and river and foot slopes were the most important factors in the formation of rock falls since the drainage system of the basin exactly followed the fault zone. The reason for this issue can be analyzed in the way that the longitudinal distance of the highest parts of this region, from the basin to the Aliabad River was lower, which has caused the slope of the basin to perform deep slices to achieve a balance in the slopes and hydrology. The southern parts of the basin are considered as one of the most susceptible basins in the geomorphologic phenomenon of rock falls and destructive cones due to the existence of alluvial formations and the lack of proper slopes and the relative reduction of the fault to the northern and eastern parts despite having significant heights and very low and low status of zonation in the risk of rock falls, and in the southwestern part of the basin, a presence of rocky outcrops in the presence of permeable cones has been also observed. This issue should be addressed to the authorities in order to avoid serious damages to the lives of the inhabitants of the basin, so that the potential risks of this phenomenon could be controlled as much as possible including: threatening communication routes and threatening rural villages and damaging electrical and telecommunication facilities, therefore, infrastructure solutions should be applied in this regard.

Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact on mortality and mortality, and the use of predictive models can be used to identify fluctuations in climatic parameters affecting disease and their prevalence and planning and Compatibility with the environment to be effective.

Using of predictive models can be consider as an effective tool in managing and controlling the diseases, reducing mortality and planning. Recent study used from Artificial Neural Networks and Logistic Regression models as an effective tool in the prediction of nonlinear processes to predict the rate of asthma admissions related to Climatic parameters in Sanandaj/Sine city. Used data during period of 8-years (2001-2008) collected from synoptic station and Toheid and Beasat hospitals in the Sanandaj/Sine city. Then, the climatic parameters and rate of asthma admissions considered as an input and output data of models, respectively.

The results of the output of two nonlinear models of artificial neural network and Logit in examining the effect of climatic parameters on the number of the asthma patients in Sanandaj/Sine showed that the monthly average parameters with high coefficient of determination (R2=0.98) of temperature (average, minimum, maximum) and QFE pressure in the artificial neural network model and The monthly average minimum temperature, QFF pressure and wind speed (in Knot) in the Logit model have had the greatest impact on the rate of asthma admissions in the city. As the wind speed in the Logit model is more effective than other climatic parameters, that it is clear with the logarithmic superiority (-0.977) and the Wald coefficient (85.616). In general, air pressure, temperature and wind speed are the most effective climatic parameters on the number of asthma patients visiting the hospital. Therefore, depending on the accuracy of the models, the above argument means that among the parameters examined, the elements are more important than other parameters in the city. As the climatic elements have a more effective role in the admission patients to the hospital, and their fluctuations will be more significant in patients' fluctuations. The effects of environmental parameters (climate and pollutants) on diseases have previously been investigated as well, so that the results of previous logistic regression have display a increase respiratory disease, vulnerability of children to asthma and an increase in allergies; In the present study, the results of Logit model (69.5%) also indicate that decrease in the average minimum temperature lead to a decrease in the number of the asthma patients, it means that the rate of asthma is more less in temperatures close to zero or higher and vice versa, the admission more higher in the colder temperature (below zero); in the other words, the more balanced the temperature has the lower the rate, and in the colder the ambient temperature has the highest the number of asthma patients. Thus, comparison the present results and previous studies show that admissions change depending on climate, geographic position and the fluctuation of the elements and then the specific geographical location and the different climatic types of a region will play a decisive role in the number of asthma visitors to hospital.

The results indicated that Artificial Neural Network model predicted the asthma admissions related to monthly minimum, maximum and average temperatures with considerable accuracy, so that the correlation between actual and predicted data is significant with 0.01 coefficient and 0.99 confidence. Also, Input parameters in the Logit method shows that the rate of asthma admissions affected by parameters of average minimum temperature, average pressure QFF and average wind speed (in knot). In other words, the logarithmic ratio of each of cited parameters is significant with β-coefficients (-0.517), (-0.734) and (-0.977), respectively, that throughout of studied parameters is wind element of effective in asthma admissions then others to the hospital. In general, Artificial Neural Network model showed more sufficiency and accuracy than Logit model. As a result, both Logistic Regression and the Artificial Neural Network methods show that climatic parameters have a greater than 50% effect on the number of asthma patients referred to the hospital (the accuracy models: 69.5 and 98, respectively). In the Artificial Neural Network model, the most accurate possible result shows the more effective role of climatic parameters of temperature and air pressure on the asthma patients. Also, filtering the parameters examined at the output of the Logistic model showed the most possible coefficients for minimum temperature, QFF air pressure and wind speed (knot), among which wind speed was the most important element. Finally, the accuracy of the models showed that the Artificial Neural Network model has a higher accuracy depending on the coefficient of determination and highest correlation. Thus, Artificial Neural Network and Logit as nonlinear methods could well predict the relationship between climatic parameters and the number of the asthma patients. Also, according to the appropriate selection of input parameters and determination of different structures in the neural network is possible to design different models with the highest efficiency and can be considered as an effective and powerful tool in estimating similar studies.