مدل رگرسیونی

In this study 31 stations were selected according to climatic parameters such as temperature and precipitation. Climatic parameters such as rainfall Monthly and 5 temperature parameters during plant growth from October to June, for 25 years of statistical period from 1982 to 2006, as well as data on wheat yield in each station, a test run test for Data was randomized and then the reconstruction test was performed on missing and missing data. In each station, the correlation coefficient between wheat yield and climatic parameters was calculated and ultimately the model Regression for stations was used to identify effective climatic parameters in estimating wheat yield. In wheat, all stations except Gorgan station had a regression model. In wheat dryland, stations of Urmia, Tehran, Sari, Yasuj, Semnan, Khorramabad had no regression model. The regional model was also calculated for the country and compared with the station models. The yield of blue wheat showed the results of the regression model. In the eastern, central, northeastern and eastern parts of the southern part of the country, the water will fall by 20-35% reduction. In the wheat field discussion, all stations in the northwest are between 30-37%, parts of southwest and south of the country will have a yield reduction of 28-35% And the southeast and north-east half of the 47-42 will increase performance. The highest expected yield in blue wheat For Isfahan station and in rainfed wheat, the most expected performance will be for Zahedan station.

Annual precipitation forecasting usually guarantees success in sustainable management of water resources and watersheds in cases like determination of rain-fed and water cultivation area and water resources consumption planning. Although precipitation does not follow a specified pattern; however, it has a correlation with some climatic parameters. If these parameters were easy to find, simple applicable models could be developed to predict annual precipitation. One of the simplest methods for predicting annual precipitation is regression models. The block box models such as Artificial Neural Network (ANN) has also been used for precipitation forecasting.  This study was an attempt to predict annual precipitation using some climatic parameters by multiple linear regression and ANN models.  

Chaharmahal-Bakhtiari and Yazd provinces are located in the southwestern and central part of Iran with semi-arid and arid climate, respectively. Mean annual precipitation of these two provinces are 560 and 110 mm, respectively. Meteorological data of the weather stations belong to 2001-2011 in Chaharmahal-Bakhtiari and 2003-2013 in Yazd province; respectively, were analyzed. Various parameters were calculated for predicting annual precipitation using long-term daily precipitation and temperature data of the meteorological stations. Among the parameters, total precipitation in the first half of the water year (R6m, mm), time to 47.5 mm cumulative precipitation since the beginning of autumn (t47.5, day), long-term mean annual precipitation (Rm, mm), average summer temperature of the preceding water year (Tsu, °C) and average temperature of preceding summer and current autumn of water year (Tsu.au, °C) that had a high correlation with annual precipitation, were used in multiple linear regression (MLR) models and artificial neural network (ANN) techniques. normalize mean square error (NRMSE) and degree of agreement (d) were used to evaluate accuracy of the models for predicting annual precipitation.

Results showed that the obtained MLR models were significant at a probability level of less than 0.01. Results also showed that both methods (MLR and ANNN) could accurately estimate the annual precipitation. Evaluation and verification of the models with NRMSE values less than 0.3 and d values greater than 0.8 confirmed the performance of the models. The best topology of the ANN network in the study was a multiple layer perceptron network with one hidden layer and two neurons and sigmoidal activation function. The findings of the study support the fact that higher temperature in summer and autumn was a sign of higher and lower annual precipitation in Chaharmahal-Bakhtiari and Yazd Provinces, respectively. Besides, higher time period to 47.5 mm cumulative precipitation from the beginning of autumn implies fewer amount of annual precipitation.  

This study showed that annual precipitation could be predicted by MLR and ANN methods in both arid and semi-arid regions with acceptable accuracy. According to the results, a rainy water year will be expected continuing a warmer summer and autumn in Chaharmahal-Bakhtiari Province; however, there will be a dry water year in Yazd Province followed by these conditions. Furthermore, if 47.5 mm cumulative precipitation takes a longer time since the beginning of autumn annual precipitation will decrease.

Thunderstorms are major climatic events due to the significant effects and catastrophic consequences on humans and the natural environment. The researches have shown that the elevation and latitude factors are two variables that can affect the occurrence of this phenomenon. Therefore, the main aim of this study is to investigate the spatial analysis of the effects of lightning and its effects on the components such as elevation and geographic extent in Iran. Apart from this fact, firstly, the monthly data of thunderstorms occurrence in 118 synoptic stations of Iran, from 1991 to 2010 on a basis from the country's meteorological organization were obtained and GIS software was produced by the annual and seasonal maps of Iran. Then, for the spatial analysis of this climatic phenomenon, the method of landing statistics of the Kriging (Universal) method was to examine its seasonal and annual status. In order to better understand the effect of Thunder hurricanes from altitude and latitude using CerveExpert software, seasonal and annual charts, along with the correlation of each production, were analyzed. The results show that the highest annual thunderstorms occur in the northwest of Iran, and the least amount is consistent with the central and eastern parts of the country. In addition, according to seasonal analysis, although the station has the highest rate at 800 to 1,300 meters, the maximum occurrence of this phenomenon varies from 0 to 2200 meters in different seasons of the stations. The overall result shows that the factor of height is slightly correlated with the occurrence of the Thunder storm phenomenon and the highest correlation is due to the latitude factor.

Soil organic carbon (SOC) content plays a key role in soil biological، chemical and physical behavior and knowledge about its state and distribution is essential for the effective and sustainable use of soil. Laboratory measurements of SOC are costly and time consuming and have not the possibility to extend the results to similar areas. Recently، the use of remote sensing data for evaluation of SOC as a simple، rapid، inexpensive and even accurate have attracted the attention of researchers. The aim of this study was to evaluate the efficiency and accuracy of ETM+ satellite images for estimating SOC using artificial neural network and regression models. In this way، the digital number values in different bands of ETM+ satellite images have been used. Both regression and neural networks were used to develop the models between organic carbon measured in the laboratory and satellite data. Finally the accuracy of these models was evaluated with R2 and RMSE indices. The statistical analysis shows that these models was fitted using digital number values in the visible and mid- infrared bands of satellite images have the highest coefficients of determination. Moreover، the neural network models have the higher accuracy and. less error than regression models.

With all the importance of water in the economy Iran is، every year floods volumes high of waters and Prolific soils of the country and access out to the desert، lakes and seas Delivers. the hydrological model can be structured according to the basin and operating characteristics affecting desired interaction phenomena and its behavior show with reasonable approximation. However، using different experimental models in the esteem at ion of runoff washbasin associated with many problems. Why regional analysis in flow، with an estimated return period specified by the physiographic an climatic chara cteristi chase wide application. In this study data from five sub basin the monthly precipitation and discharge in the river basin kashkan scale With good dispersion in the basin are is. Runoff generated from the multivariate regression modeling has been at tempted models for evaluating models developed using. A number of criteria and indicators in clouding، correlation coefficient، standard error، the relative error of estimation and verification، the mean absolute percentage error، Relative mean squared، error squares Square of the mean square error، the average absolute error is used. Results the multiple regression shows the I all period return Different the original factors Runoff Production factors maximum monthly rainfall، Area، time to focus، Coefficient Compaction are.

Introduction Estimating correct volume of sediment in a fluvial system is one of the most important issues in water engineering, river engineering, water resources, facilities, structures, water and environmental projects and programs for the development of them. The bed load transfers by saltation and suspension forms according to the sediment particle size. The sediment load is one of the most important parameters of hydraulic projects, a useful indicator of soil erosion, and watershed's ecological environment. Several physical experimental methods for estimating the sediment in a watershed has been developed. Experimental methods are based on estimated sedimentation basin characteristics such as area topography, land cover, climate and the qualitative characteristics of the firm (Zhou et al.2002). Those can estimate indirectly sedimentation rates in the Delta or the Vessels (Verstraeten et al, 2004). These methods due to the simple structure, simple mathematical calculations and are able to work in a widely input data (Zhou et al.2007). However, linear or nonlinear regression model can use for suspended sediment load with relative accuracy of computational models which provide simple experimental models. In recent years has occurred a great interest to investigate the possibility of using artificial neural network systems. Neural networks are powerful computational tool in organizing and establishing the relationship between the various intelligence capabilities. The use of the mapping capabilities of these systems in a multi-dimensional spaces and the analysis of the issues without resorting to the relations sophisticated mathematical difficulty can be useful in engineering. Artificial neural networks have been successful in dealing with issues such as system identification, function approximation, optimization and anticipating the results. Artificial neural network system based on data flexibility with different weights to the establishment of neural connections during the learning process is able to recognition complex and ambiguous issues especial issues that are not easily expressed in mathematical relationships. A river system is an open system where the involvement of sophisticated forms of communication. The inherent characteristics of a basin and external factors affect the behavior of the river. The existence of multiple interactions, including the interaction moves and transported sediment and geomorphology of the basin parameters is important. Watershed is the most essential natural scale regional units. An independent identity has defined the concept of river basin as a spatial unit has an effective role in the classification of human activities (Ramsht 2005). The purpose of this study was to develop modeling using artificial neural network approach to geomorphic agents and compare its performance with multiple regression methods. To the extent predicted by the models of sediment basin Comparison of the calculated values with observed values is explained by its comparative advantage. 2- Methodology First, for removing diagnostics data calculate the residual R student index by using SAS software. Then were evaluated training data set and testing data set from wholly of data. The Statistical Modeling and Artificial Neural Networks are helping with SPSS software. Statistical methods used and designed to model both simple and multiple regression analysis. In simple regression best fit of the relationship between flow and sediment is taken of the relationship linear, inverse, exponential, logarithmic, quadratic, and the power basis of degree decision. This method measures the curve of known deposits or SRC. This method is called MARS for short to multivariate adaptive regression sp-line. In the flow vector the power supply to the large number of independent variables into the geomorphological parameters using stepwise multiple regression analysis method to determine the model parameters have to be removed excluded variable or ineffective variable. So a geomorphological multivariate model is designed. In other words, each of the parameters in the geomorphology of the basin as a data point are being integrated with vector data such as statistics in a flood of new data are converted into a vector and new vectors is obtained base on multiple regression fit. Artificial neural network model is used by utility neural network option of SPSS software. Neural network designed in this study were divided into two types depending on the input data. The first type of artificial neural networks designed using the instantaneous flow and sediment. Instantaneous flow in the network as the input and output system is designed as a moment of deposition. NGANN geomorphology of this network is called neural network for short. The second type of artificial neural networks designed using the elements used in the method of feeding is in March and the geomorphic parameters of this network is called for short GANN. 3- Discussion Two statistics factor to evaluate the performance are RMSE (root mean square of error) and R square (determination index) of different models is possible. These factors can determine the value of the preferred models to the observed values and the predicted values. Notice to RMSE values and the R square shows relative advantage of models. The highest significant determination index, and the lowest error, is introduced the best method. To the value of the preferred methods, is determined respectively GANN, NGANN, MARS and SRC rate determination coefficients with 0.86, 0.83, 0.81, 0.76 and values of the error estimate 1.815, 2.031, 2.142 and 2.359. Results showed the GANN estimated better with highest coefficient of determination (R2) of 0.862 and root mean square of error (RMSE) of 1.815 in compression to NGANN with 0.827 for R2 and 2.031for RMSE. performance value of regression models were weaker than ANN models with R2 of 0.759 and RMSE of 2.395 for SRC model And R2 of 0.811 and RMSE of 2.142 for MARS model. Therefore it was revealed not only ANN models but also regression models have performance value when combine with geomorphologic parameters. 4- Discussion Comparison of statistical methods and neural network models shows neural network significant preference values ​​in the estimation of daily moment sediment estimation. The difference between the performance of artificial neural and statistical methods can be defied with the ability to estimate and predict artificial neural networks for non-linear approximation with a low volume of data. However, the performance and accuracy of the regression methods follow of sample size and it challenge of statistical models. The artificial neural network models design by the low number of samples. Conclusion presented and ANN model by geomorphology parameters designed to help the River Basin behavior. It is worthy of the network with back propagation algorithm learning with one hidden layer and 2 neurons in the hidden layer with high ability to flow and sediment load simulation is running with minimal restrictions. The results of the modeling of sediment load in the present study also confirmed that the results of previous studies in the review of previous studies presented in the introduction. To develop and improve deposition load modeling is necessary applying geomorphologic parameters of basin. The geomorphological characteristics of the basin are an essential element of effective and recommended for hydrological modeling.

Large areas of the ice-free continental area of the Earth are underlain by karst developed on carbonate rocks and roughly 20–25% of the global population depends largely or entirely on groundwaters obtained from them (Ford & Williams, 2007, 1). So that karst system has a critical role on the development of human communities all around the world. karst evolution is a complicated process and relates to numerous factors such as lithology, hydrology, hydrogeology and etc. Karst resources have widespread scientific, cultural and economic values. In developing countries, human activities threat these valuable and non renewable resources seriously. Nowadays decision makers in their plans for karstic areas in order to protect these valuable resources, regarding the intrinsic vulnerability of karst systems, attempt to propose methods and models for determine the degree of karstification and its sensitivities. Regional assessment of karst system vulnerability especially in the watersheds is a useful tool for management and protection of karstic areas and helps local authorities and decision makers to choose best policies to use karst recourses. In this paper a well known karstic area, called Shaho in the NW Zagros, using a multiple linear regression model has been studied for evaluating its vulnerability and design vulnerability maps.

One of the most important main environmental problems is soil erosion and its affects. Eutrophication، low oxygen levels and `high nutrient (nitrogen and phosphorus) concentrations in reservoirs، channel and other water courses، is a common water pollution feature. Phosphorus (P) is one of the major plant nutrients and also the major nutrients controlling eutrophication of surface water. The aim of current study was to quantities evaluation of the effect soil erosion on available phosphorus loss in Kojour watershed. Discharges resulting from storm events were sampled at one fixed point of river using the manual sampler. Results showed that minimum and maximum phosphorus loss through 7 rainfall events was 321and 6707 g respectively with an average value of 2620 g. Soil erosionThe result of modeling among phosphorus loss، sediment yieldrate in this period was dramatically affected on the loss of phosphorus. The result of modeling among phosphorus loss، sediment yield and runoff volume indicated that phosphorus loss didn’t estimate by discharge، while sediment concentration can estimate loss of phosphorus with estimation error of 26%. The results could facilitate the application of given methods obtained in the present study to other ungauged watershed with similar conditions and leading to the suitable soil and water management

Introduction The base of analysis of applicative geomorphology is according to systemic stand point. Systemic geomorphology is based on recognition of geomorphic forms and processes and relationships between them. Efficiency of this stand point, when there are linear and non linear reciprocal relationships between parts and elements of system, is very important. The studied feature in this research is Barchan dunes in the south of playa Chah Jam. In studied field, hundreds of barchans can be found; generally all of them have same size and shape. These landscapes are mobile dunes which constitute a considerable threat to infrastructure in study field with their mobile sands. They move over communication ways of region, phone lines and even in to Damghan city, and also disturb agricultural fields and intensify desertification problems in study area. Barchan dunes are open and natural systems and are capable of being modelled in different levels. These dunes are observed where sand availability is low and prevailing wind direction is quite constant. In this paper we have tried to describe morphologic characterizes of the Barchan dune using mathematical and statistical modelling process via morphometric parameters and parabola characterizes. The obtained results from this study can be helpful in the systemic management of desert regions and also can be benefit in the plans of stabiles of trombone sands and recognition of critical regions from view point of windy erosion. Materials and Methods The studied area (Chah Jam erg) is located in the south of playa Haj Aligholi, in central part of Semnan province, Iran. Studied field is bounded between latitudes 350, 45َ to 35َ0, 50َ N and between longitudes 540, 40َ to 550, 10َ E. For measurement of morphological parameters, at first, we have been defended a model, and Barchan morphometric characterize are measured along 10 transects in nature that these transects were covering entire extent of Chah Jam erg. Mathematical modelling of the Barchan shape is performed via its simulation in the coordinate system and using non linear equations of the parabola that it has been gotten two equations for windward side parabola and slip face parabola. Then for recognition of type and intensity of relationships between them, we have studied and reexamined these relationships, using SPSS software and regression analysis techniques. For this achievement, at start, we have reexamined linear and nonlinear, simple and multiple variable regressive analysis technique such as linear, power, exponential, cubic, logarithmic, multiplier and etc, and then we have been selected the most important and the most suitable of relationships. Totally, in this study, the most important and the most suitable of relationships are following from power relationships. Results and Discussion Using Statistical modelling, relationships between mathematical and morphological parameters of Barchan dunes has been determined, that these relationships show a significant power relation with maximum of determination index and minimum of estimation error. One of the most important results of this study is the statistical and mathematical modelling of Barchan dunes and converting of mathematical method to statistical method and vice versa. One of the main characteristics of morphology of Barchan dune is its parabolic shape, that this characteristic is influenced by morphology of crescent and aircraft of Barchan. Therefore for mathematical modelling of Barchan shape can be used parabola equations. On the other words, two-dimensional modelling of Barchan surfaces (regardless from its spatial characteristics) is possible through parabola equations. The recognition of morphologic characteristics of Barchan can act as an index in order to determine the condition of Barchan system and also shed light on its trends. Furthermore, the investigation and exact estimation of these parameters is a consequence from rate of threat, destruction and annual movement that their assessment determines different region from the view point of environmental management and prioritize them. Therefore, obtained results from these methods can be very important for investigation of shape and recognition of behavior of Barchan dunes in nature. Conclusion In this paper we have used statistical and mathematical modeling methods and have represented models that using them, can easily account morphological parameters of Barchan dunes. Therefore the results of this study provide possible of exact and rapid estimation of Barchan morphometric and morphologic parameters. The combination of two methods of statistical and mathematical modelling is a fundamental infrastructure for simulation of Barchan dunes geomorphologic characteristics in the laboratory and imaginary circumferences. Focal distance is one of the important parameters of the parabolas of Barchan dunes that shows its different situations with each other. Results show that if the focal distance of the Barchan dune decreases, its spout becomes closer and the divergent state of Barchan horns decreases, too. Barchan dunes that have farther focal distance are wider and have extensive dimensions and more volume.

Aeolian sand dunes originate from reciprocal interaction between wind flow and sand bed. Barchan dunes are one of the Aeolian compressive forms and are formed in areas that there is not enough sand to cover the entire surface and wind flow comes steadily from the same direction throughout the year. These features have been formed from quick sand. One of the most important of their characteristics is dynamic and lateral movements, that some parts of them are regarded as crisis and threatening focuses for urban and rural settlements, economic and military centers and communication ways. Totally, Barchan dunes move proportionally to the wind velocity and inversely proportionally to their height. Their movement rate is subject to wind velocity, three dimensional morphology and interaction between them. One of the most common of Aeolian features in south of Haj Ali Gholi playa are Barchan dunes. In this study, morphometric parameters of Barchan dunes and their annual movement rate (from 1387/1/10 to 1388/1/10) have been measured via pique. Using regression analysis method, type and intensity of relationship between movement rate and morphometric parameters have been investigated and models have been presented in order to estimate their movement rate. Using these models can assess annual movement rate of Bachan dunes in the field area easily. The obtained results show that relationships between movement rates and morphometric parameters consistent with simple linear, power and exponential relationships. The obtained relationships from relation study between movement rate and Barchan morphometric parameters represents a maximum significant exponential reverse relationships of movement rate with height parameter with determination index of 0.915 and standard error of estimation of 0.133. The recognition of morphometric parameters of barchan can act as an index in order to determine the condition of barchan system and also shed light on the its trends. Furthermore recognition and estimate of these parameters is a consequence from rate of threat and distribution and provide for environmental managers with the same, that their investigation determines different region from the view point of environmental management and prioritize them.

Introduction Focusing on the problem of estimating sediment transport over a fluvial system is one of important aspects in environmental management and applied geomorphology. Artificial neural networks (ANNs) have been applied to runoff-sediment modeling and flood forecasting. One of the most important parameters in runoff-sediment process is geomorphic characteristics. Generally, two methods exist for modeling: function-driven and data driven modeling. Function-driven modeling is based on fitting a suitable function such as regression curves but data-driven modeling such as neural networks work by giving weight to each data in a try and test in a frequently algorithm process.Therefore, neural networks are alternative and complementary set techniques to traditional models. The purpose of this paper is to apply and compare both regressions and neural networks for runoff-sediment modeling in a watershed scale by geomorphic parameters and without them. Therefore, this study evaluates performance of two artificial neural networks-a geomorphology based artificial neural network (GANN) and a non- geomorphology based (ANN); and two regression models, power relation (PR) and multivariate adaptive regression spline (MARS), for prediction of suspended sediment. Material and Methods The study area comprises of the Plasjan River, in Eskandari watershed, north east of Zayandeh roud basin, Esfahan, Iran. The watershed has an area of approximately 1640 km2. The recorded data of runoff and suspended sediment values are available, measured at one station in outlet of the Eskandari station. Data set of flood runoff and sediment at the same time for the years of 1995 to 2006 were provided by the Esfahan Water Agency. Several geomorphic parameters were used to create geomorphic models such as relative relief, form index and drainage density. There are several stages processes to develop artificial neural network for simulation application as follow: 1. Data selection: gathering an appropriate data set. 2. Selection of an appropriate predictand: to decide what is to be modeled 3. Artificial neural network selection: to select an appropriate type of network and choose a suitable training algorithm. 4. Data preprocessing: to process the original data in terms of identifying suitable network inputs (predictors) and perform data cleansing as appropriate, for example, if necessary, remove trends or seasonal components. In addition, one must normalize and split the data into training, validation and testing data sets. 5. Training: to train a number of networks using the chosen training algorithm and preprocessed data. 6. Using appropriate assessment criteria evaluate the model produced and select the best solution for subsequent implementation. It was revealed that the feed-forward ANN model with back propagation algorithm performed well for both the GANN and ANN models. The sediment loads predicted by these models were compared with observed data for the same watershed and compared with regression models including power regression and multivariate adaptive spline with evaluation indexes of root mean square of errors and determination index. Results and discussion Development of regression and neural network and using geomorphic parameters besides to runoff and sediment showed noticeable results. However, the GANN predicted better with highest coefficient of determination (R2) of 0.98, root mean square error (RMSE) of 4.49 in comparison to ANN (R2 = 0.96, RMSE = 5.35). The regression model performance was inferior (R2=0.89, RMSE=8.66) for MARS and (R2 = 0.81, RMSE = 15.05) for PR to the ANN models. Therefore ANN technique especially GANN is a powerful tool for real-time prediction of sediment transport in a complex network of rivers. Conclusion Neural network (NN) is a suitable tool for simulating the behavior of sediment transport in a river system. One major advantage the NN approach has over traditional input-output modeling is that it makes fewer demands of data. Unlike multiple regressions, where the constraints preparation the number and distributions of data, are often simply used, NN do not make assumptions about the statistical properties of a data set. Data for several variables can be use flexibility on temporal and spatial scales. Therefore NN find a non-linear pattern carefully that it does not with traditional methods. An advantage of the results is the effective rules of geomorphology parameters in modeling procession fact which illustrate importance of them in vision of river’s behavior in a catchment.