algorithms

Part of the development of tourist villages depends on the environmental parameters and their optimal management. In this context, geophysical damages can be a clear example of factors and natural hazards that are effective in the development of tourist villages. Therefore, the recognition and analysis of tourist villages in terms of geophysical vulnerability can be very important, and action should be in line with environmental planning and crisis prevention. In this research, this topic has been done for Iran's tourism target villages based on fuzzy scenarios in GIS. The analytical-quantitative research method compiles and analyzes data based on fuzzy logic scenarios (optimistic, pessimistic, and balanced) in GIS. In this research, geophysical variables, including active faults, soil texture and granularity, flood plains, protected areas, Landslide points, Land slope, and geological formations, were used. The results showed that more than 543 Tourist villages of Iran in the pessimistic scenario have a maximum geophysical vulnerability with a value of one; 201 villages in the balanced scenario have a maximum vulnerability with a maximum value equal to 0.7, and in the optimistic scenario, 98 villages have the maximum geophysical vulnerability with a value of 1. Also, examining the spatial distribution pattern of Iran's tourist villages in terms of geophysical vulnerability in three pessimistic, balanced, and optimistic scenarios through Moran's index showed that the dominant spatial pattern is a cluster. As a result, the vulnerability of tourist villages is influenced by various geographical factors such as roads, formations, topography, landslides, etc. Consequently, significant touristic villages are exposed to geophysical risks, which should be adopted according to the priority for risk management and prevention

Almost most of the installations located in natural beds face many threats over time, and in order to reduce the damage, it is necessary to identify the threatening factors and use the results in the appropriate location or in taking measures to reduce the damage. Today, the increase in consumption Gas has caused an increase in the density of the gas transmission pipeline network and, as a result, an increase in its potential risks. The first step in risk analysis is to identify the effective factors in the occurrence of accidents and breakdowns on the pipeline. According to the environment around the pipeline, various factors cause pipeline damage and accidents. After identifying the damage factors, the amount of damage caused by each is calculated and the results are expressed in the form of risk. It is possible to establish a connection between the risk estimation process and the geographic information system and make appropriate zoning. It was determined by using models and geographic information system. Therefore, in this research, the process of estimating environmental risk with geographic information system and using hybrid-fuzzy algorithms has been investigated. Valuable information such as risky components can be determined by assessing the risk of gas pipelines, and a suitable response and strategy can be used to reduce or even eliminate it. In order to achieve this goal, it is necessary to use a suitable technique that can accurately and reliably assess the existing risks, so that planners and managers can act with a wider horizon and a lower risk factor towards the optimal management of gas transmission lines. The extent of gas lines in Tehran and Qom province and considering the environmental and natural characteristics of the two provinces, it is very important to assess the amount of damage. In this research, MATLAB version 2019b software was used in order to assess the risk of the gas pipeline using the multi-layer perceptron neural network model. Due to the fact that the number of input nodes and hidden layers are varied in the specified range, the optimal number of input nodes and hidden layers was determined by model selection criteria on the test data and the WIC model was used. Due to the existence of 11 criteria in this research, 11*11 modes were created. Also, in this research, the input layer has 6 neurons and 1 neuron in the hidden layer and the algorithm used is Levenberg- Morquardt according to the purpose of the research and high accuracy. In this model, there were 740 data, 70% of which were used for training, 15% for testing, and 15% for evaluation. For risk assessment, criteria were weighted by VIA method. In this method, using the feature eliminate process, a criterion was removed in each step and the network error was measured. In this research, in addition to the MLP model, the Random Forest model was used. In this method, an estimate of the classification error can be obtained based on the training data. The number of trees should be enough to stabilize the error rate and the additional index that is created in the RF method. To estimate the feature importance, first the OOB components are run among the trees and the votes are counted for correct classification. Then, the prediction accuracy is obtained many times after randomly changing all the values of this feature while all other features are the same. In order to assess the risk of the gas pipeline using the random forest model from MATLAB version 2019b software and from the model Regression and RF Regression function were used. In this model, the selection of training and test data is random and 740 points are samples, 80% of which include training data and 20% of test data. In this model, the number of decision trees used by the tree bagger function is 500. To check the validity of the models, the estimated values obtained from the networks and the measured values in the test phase were used. To validate the model, the root mean square error (RMSE), mean error of exploitation (MBE), and mean absolute error (MAE) were used.According to the results of ANP landslide index, 0% of the area is in the low risk class, 17.28% in the medium risk class, 73.14% in the high risk class, and 9.58% in the high risk class. Therefore, it can be said that 19.008 km of the investigated area are located in parts with moderate vulnerability, 80.454 km with relatively high vulnerability and 10.538 km with high vulnerability. According to the results of the Fuzzy model landslide index, 41.85% of the area is in the low risk class, 11.60% in the medium risk class, 22.52% in the high risk class, and 24.03% in the high risk class. Based on the landslide criterion and the results of the fuzzy model, it can be said that 46.035 km with low vulnerability, 12.76 km with moderate vulnerability, 24.772 km with relatively high vulnerability and 26.433 km with vulnerability. In this research, the systematic error (MBE) of the MLP model is estimated to be 0.002812, and the absolute error of the model is 0.042168. The RMSE error rate is 0.05020. The systematic error (MBE) of the RF model is -0.151848. The absolute error of the model is 0.179101. The systematic error (MBE) of Fuzzy_ANP model is -0.16893. The absolute error of the model is 0.170337. The RMSE error was 0.12262.

Flood is one of the natural phenomena and one of the most important and destructive hazards in the world, which is associated with loss of life and property every year in different parts of the world and Iran. Climate change has consequences and effects on global warming, reduction of  Agricultural production, changes in the diversity and vegetation of pastures, changes in groundwater levels, the occurrence of social and economic problems, and so on. The overall goal of this research is to zoning and model areas at risk and  flood risk for changing climates. The prevailing approach of the research is the approach of climate change and its effects on the hydrology of Gorganrood watershed. The key goal of the current research is to model and forecast regional flood risk under climate change conditions using fuzzy analysis algorithm, hierarchical analysis and SWAT model in the watersheds of Golestan province. In fact, it is possible to plan the water resources of the region more precisely and help to calculate the more precise management of the transfer of the region along with other environmental variables, including this research. There have been many research studies on the phenomenon of precipitation in Golestan province and Iran, but none of them have examined this climatic variable with the perspective of climate change together with environmental variables.                                                                                                      

Gorganrood Basin with an area of ​​10197 square kilometers is one of the northeastern basins of the country, a large part of which is located in Golestan province. It is bounded by the Atrak catchment area and the Caspian Sea and Qarahsu catchment area from the west. In the present study, flood zone changes in Gorganrood watershed based on the use of meteorological organization (synoptic) station information with a 30-year statistical period(1989 to 2018), land use, vegetation, topographic moisture index, slope, altitude, land lithology, Distance from river, river density, erosion, soil science, runoff, simulated data The average rainfall of HadCM3 model in LARS-WG under SRA1B scenario is estimated between 2011 and 2045. The evaluation criteria used in this study are defined based on parameters such as ME, RMSE, ASE, MSE. The most important criterion for estimating estimates is the square root of the mean error (RMSE). (ME) is the mean of the errors or the mean difference between the estimated value and the value observed at point I. The SWAT model is an extension under ArcGIS software that simulates the main hydrological processes including evapotranspiration, surface runoff, deep infiltration, groundwater flow and subsurface flows by the simulator model. The type of land use in the region, the type of vegetation in the region, the slope values ​​and the length of the slope in different areas, which is used through the Geographic Information System (GIS). The input spatial variables of the SWAT model in this study include the digital elevation model (DEM) information layers, the soil layer with soil texture information, and the land use layer with its descriptive information. Hydroclimatology and numerical variables of SWAT model include daily precipitation, minimum and maximum temperature related to stations inside and outside the basin and the average daily runoff of the basin outlet along with their geographical locations. Simulating large and complex areas with different management strategies can be done without spending a lot of time and money.                  

The results for SRA1B scenario therefore it can be concluded that in general for the next period, the  area under study will face a decrease in average flow rate. By analyzing the obtained results, it can be inferred that the phenomenon of climate change will have tangible effects on the studied  area over time and will change the values ​​of temperature and precipitation parameters and will reduce winter precipitation and increase the temperature of the studied area. results of the future discharge simulation show a decrease in runoff for October under the SRA1B scenario. Therefore, according to the results, it can be stated that in general, the SRA1B scenario predicts a decrease in discharge for the next period compared to the observed discharge. The simulation results of the basin output flow and observational flow measured by architect R2 and NS as well as the uncertainty parameters r-factor, pfactor were evaluated and analyzed. The optimal values ​​of R2 and NS coefficients are one and one. One of the goals of the SWAT model is to To reduce impotence. So that most of the observational data are at the level of 95%. In research, NS coefficient greater than 0.5 and p-factor greater than 0.5 have been introduced as satisfactory values. Examining the changes, it is expected that the runoff will decrease with increasing scenarios. This trend has decreased in RCP4.5, although its limit rainfall has also increased, and in RCP8.5 (with more rainfall) the runoff has increased. The reason for this change is that the incidence of precipitation in RCP4.5 was in summer and early autumn, which despite the high evaporation and lack of snowmelt (according to RCP8.5 which in spring coincides with snowmelt) runoff is less than the next  scenario.                             

Flood risk zoning in the Gorganrood watershed, 345 villages with a population of 275,312 people are at high risk of flooding. square, it has a relatively low potential equal to 1425.46 square kilometers, a very high degree of flood risk equal to 884.68 square kilometers, and finally, an area with a low flood potential equal to 623.12 square kilometers from the entire surface of the Gorganrood watershed. It can also be concluded that most of the basin level equal to 89% is at risk of severe flooding. In all the stations, according to the predictions made by the LARS-WG model, the rainfall for the future scenario has increased in some months and decreased in some months compared to the base period . So that in the summer month, the rainfall will decrease and compared to the winter season, the rainfall will increase compared to the base period. The comparison of the monthly rainfall of the base period (2011-2007) with the rainfall simulated by the HadCM3 model for the SRA1B scenario (2011-2045) also shows a significant increase in rainfall in this series of scenarios in the months of September, October and November. It can be seen with a few changes. which can be investigated in terms of the effects of this phenomenon by studying the average percentage of precipitation changes for the SRA1B scenario in 2 periods; 2011-2045, 2045-2065 also shows the changes in rainfall from August in the period, 2011-2045 to 37.5% in November of the same period compared to the base period. The very high increase of precipitation changes in November can also be investigated and pondered for the cause of autumn floods.

The increasing incidence of dust storms indicates the dominance of desert ecosystems in each region. Therefore, in order to properly control and manage dust storms, it is necessary to be aware of the temporal-spatial changes of this phenomenon and the need to predict and model it. In this study, in order to predict the variable frequency of days with dust storm (FDSD), the results of two hybrid methods under the titles of support vector-wavelet (W-SVM) and support vector-artificial plant algorithm (AF-) (SVM) was compared with the individual support vector machine (SVM) model. For this purpose, hourly dust data and codes of the World Meteorological Organization were used on a quarterly scale with a statistical period of 40 years (2018-1980) in five selected synoptic stations of Sistan and Baluchestan province. Explanation coefficient, root mean square error, mean absolute error value and task strain coefficient were used to evaluate and compare the models. The results of goodness-of-fit indices in the training and testing phase showed that the hybrid structures used provide acceptable results in modeling the FDSD index. Support-wavelet car vector hybrid model with correlation coefficient (R2 = 0.911-0.984), root mean square error (RMSE=0.314-0.397), mean absolute error value (MAE=0.236-0.335) And clutch saturation coefficient (NS = 0.927-0.965), had better performance than other models used in predicting the FDSD index. The results of this study can be effective in managing the consequences of dust storms and desertification programs in the study areas.