linear regression

The use of geospatial techniques for mapping soils is broadly covered by the term digital soil mapping (DSM). Soil maps have considerable significance as basic maps in many environmental and natural resources studies. Digital soil maps are based on the relationship between environmental variables and soil properties. With the development of computers and technology, digital and quantitative approaches have been developed. Continuous utilization of agricultural lands regardless of the land suitability caused soil destruction. Also, incompetency in custom methods, invention geographic information system (GIS), and remote sensing (RS) techniques cause erupt and use of digital soil mapping.

The study area is approximately 5000 ha which is located in the west of Heris region of East Azerbaijan province, Iran. In the first study, the potential of different models to predict soil classes at different taxonomic levels was investigated. According to semi-detailed soil, survey and using stratified random sampling method, 50 pedons and 50 augers with an approximate distance of 1000 m were excavated, described and soil samples were taken from different genetic horizons. Based on the pedon descriptions and soil analytical data, pedons were classified up to the family level. Different machine learning techniques, namely boosted regression tree (BRT), random forest (RF), artificial neural networks (ANNs), and multinomial logistic regression (MLR) were used to test the predictive power for mapping the soil classes. After preparing the soil properties maps and checking their accuracy, these maps were used along with auxiliary parameters for estimating soil classes using an artificial neural network model in the R software. Finally, the accuracy and uncertainty of the model were evaluated by overall accuracy and confusion index, respectively.

Results showed that the different models had the same ability for prediction of the soil classes across all taxonomic levels but a considerable decreasing trend was observed for their accuracy at subgroup and family levels. The terrain attributes were the most important auxiliary information to predict the soil classes up to the family level. The main goal of the second study was to predict soil surface properties (pH, electrical conductivity, gypsum, organic carbon, calcium carbonate equivalent, coarse fragments, and particle size distribution) using ANNs, BRT, generalized linear model (GLM), and multiple linear regression (MLR). Among the studied models, GLM showed the highest performance to predict most soil properties whereas the best model is not necessarily able to make an accurate estimation. Also, the terrain attributes were the most important environmental covariates to predict the soil classes in all taxonomic levels, but they could not display the soil variation entirely. This shows that the unexplained variations are controlled by unobserved variations in the environment, which can be due to the management over time. Results suggested that the DSM approaches have not enough prediction accuracy for the soil classes at lower taxonomic levels that focus on the soil properties affecting land use and management. Results showed that the entry of more details in the soil classification at the lower levels of the Soil Taxonomy system while increasing the number of classes, leads to decreasing the overall accuracy and increasing uncertainty. It is noticeable that the ANNs model has a good accuracy up to the great group level through the acceptable level of overall accuracy (i.e., 75 %), hence it has a high degree of uncertainty. Therefore, the accuracy of the model could not be effective in its selection through the modeling process; however, paying attention to its uncertainty is also very important along with the model error.
 

Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas. Further studies may still be required to distinguish new environmental covariates and introduce new tools to capture the complex nature of soils. Accordingly, we suggest using the other methods of soft computing for modeling in plain areas or low relief regions. Finally, the use of DSM methods is increasing over time and will eventually be considered as distinct and novel techniques.

Water resources face challenges such as climate change and human activities. Sustainable water management is extremely important to solve this problem. More and more people are using artificial intelligence, especially machine learning, to predict and manage water quality. These AI methods are excellent at identifying patterns in water data and improving water quality management. This study examines the water quality of the Maroon River using a combination of factor analysis and machine learning. Data on various water quality parameters were collected from three stations over a period of ten years and the water quality index was calculated. Then, different machine learning algorithms were used to predict the water quality index. In a further step, factor analysis was performed to extract the important features of the input for the optimal algorithm. The performance of the studied algorithms was determined at each step using evaluation criteria. The results showed that in the first step, the Random Forest algorithm (R2 (0.78), RMSE (2.65)) had the best performance in predicting water quality index. It was also found that among the three algorithms studied, nitrate is the most important input parameter, while acidity is the least important. By reducing the number of inputs to 3 important parameters, the performance of the Random Forest algorithm (R2 (0.74), RMSE (2.86)) almost reached the level of 8 input parameters. Combining insights from factor analysis and feature importance analysis can provide a more comprehensive understanding of the complex relationships among water quality parameters and help develop more effective water management.

This study addresses the challenge of measuring soil cation exchange capacity (CEC), a vital factor influencing soil fertility, by exploring the impact of grouping soil samples based on different characteristics on the performance of estimation models. Recognizing the difficulties associated with traditional CEC measurement methods, the study employs a cost-effective and rapid approach using various models and equations. The research, conducted at Bu Ali Sina University in Hamedan, utilizes a substantial dataset of 45,948 soil samples from the standardized database of world soils. Soil samples are initially categorized into different groups, and nine estimator variables are examined across 11 models for the entire dataset and specific classes within each group. These variables include soil texture components, organic carbon, calcium sulfate, calcium carbonate, bulk density, base saturation percentage, total exchangeable base cations, and soil reaction. The results demonstrate that grouping soil samples, especially based on texture classes, significantly improves the performance of artificial neural network models, with a remarkable 87% relative improvement coefficient in the test section. The study reveals that data grouping enhances the model's estimation capabilities, as evidenced by reduced root mean square error (RMSE) values in the test sections for different texture classes. In conclusion, the findings suggest that utilizing functions derived from grouped data offers an effective and cost-efficient method for estimating soil cation exchange capacity. This approach provides valuable insights for soil fertility management, offering a simplified yet accurate means of assessing this critical soil parameter.

Remote sensing is a fast and cost-effective solution in preparing and presenting this data to climate models due to the difficulty of preparing snow meteorological data in the field. The presence of clouds is one of the problems of satellite images that cause temporal-spatial fragmentation of snow data and increase its resolution efficiency. This study aims to provide a suitable framework for removing the effect of clouds on satellite images and generating the satellite snow metering data without disturbing cloud effects. For this purpose, first, daily MOD10A1 images of the MODIS sensor products were refined (temporally and spatially) using local snow depth data, average temperature, and daily rainfall of the Lake Urmia catchment and western part of the Caspian Sea basin. A multivariate linear regression model application showed that the normalized snow differential index (NDSI) values correlate with existing snow metering station data (r= 0.85 and RMSE= 0.047). Accordingly, the cloud-covered areas in the MODIS images were replaced with the values obtained from the model. Then, new NDSI values were calculated using geostatistical methods and the location of each pixel's location. By examining the relationship between the NDSI and the cumulative snowfall from January to March 2016, it was found that replacing the primary satellite images with corrected images can increase the R2 from 0.63 to 0.81. Therefore the proposed methodology could improve the accuracy of satellite snow metering.

The Objective of this research is to provide a model to calculate flood discharge values with a certain return period by using the geometric features of the watershed and by providing multivariable models.To understand flood occurrence in the Gorganrood watershed, we studied 11 hydrometric stations in Golestan province. Various influential parameters, including physiographic factors, vegetation, land use, soil science, geology, and weather including 31 independent variables were selected in this research. Data analysis and flood modeling were done by the multivariate regression statistical method. In this way, flood regional models using the Enter method have higher accuracy and precision than the models obtained using the Forward method. In general, 13 variables out of 31 independent variables included in the multivariate regression test have been identified as the most important and influential factors in the occurrence of floods in the Gorganrood watershed. The effective variables include: Gravel's coefficient, percentage of the area of permeable formations, watershed slope, concentration time, watershed area, flow velocity, percentage of land area with a north direction, percentage of land area with a south direction, percentage of pasture land area, percentage of agricultural land area, percentage Waterway density is ranked 3, as are average annual temperature and maximum annual rainfall.

By using these relationships (models), it is possible to obtain the values of the maximum instantaneous discharge with a return period of 2, 10, 5, 25, 50 and 100 years in the basins without statistics under the sub-basins of Gorganrood.

Evaporation is one of the main components of hydrological cycle and one of the effective climatic variables in arid areas such as Iran. Accurate estimate of evaporation rate plays an important role in sustainable development and optimal management of water resources. Evaporation is one of the essential processes, because it depends on meteorological variables such as solar radiation, air temperature, wind speed, relative humidity and atmospheric pressure, which are related to the topography and the climate of the region. Class A pan-evaporation is one of the standard and direct tools for measuring evaporation, which is used all over the world due to its ease of application in determining evaporation. However, in most stations accurate evaporation recording is not practical due to instrument limitations and maintenance problems. On the other hand, the temporal and spatial distribution of evaporation stations compared to meteorological stations is limited, so according to the problems mentioned, the use of meteorological variables in estimating the rate of evaporation from the pan will be useful. In different regions, the impact of different climatic factors on changes evaporation from the pan has not be fully understood, so the relatively accurate estimation and prediction of this phenomenon is an effective step in the relevant fields. In recent years, for estimating the amount of evaporation from the pan, a variety of intelligent systems and software calculations such as data mining methods have been developed.

In this study, meteorological data of Tabriz station in the period of 2003 to 2018 have been used to estimate the evaporation values from the class A pan. For this purpose, a simple correlation between meteorological variables and evaporation from class A pan was created and based on the result of this correlation, in the studied station the minimum temperature and relative humidity were inversely and the maximum and average temperature were directly affected by evaporation. Thus, ten combined scenarios were defined and modeling was performed using Support vector regression (SVR), Gaussian process regression (GPR), M5tree, Random forest (RF) and Linear regression (LR) methods. It should be noted that in this study, 70% of the data were selected for training and 30% for testing. Finally, the performance of each method in estimating evaporation values was evaluated using root mean squared error (RMSE), mean absolute error (MAE), Nash- Sutcliffe coefficient (NS) and Akaike information criterion (AIC).

The results showed that GPR10 method with RMSE = 1.90 mm/day, MAE = 1.48, NS = 0.81 and SVR10 method with RMSE = 1.92 mm/day, MAE = 1.51, NS = 0.8 had reasonable performance in estimating the values of daily evaporation from class A pan. The GPR method showed its higher capability to estimate daily evaporation values in all definition scenarios with the least error and the most accuracy. The SVR model with appropriate results was in the second place. The results of statistical parameters for random forest model were even weaker than the results of linear regression. In general, scenario number 10 with all meteorological variables and scenario number 1 with only the input minimum temperature variable had the best and weakest results among all defined scenarios, respectively. Scenarios 6 to 10 have more accuracy and less error and modeling structures with the least number of variables has the least accuracy. Also, wind speed and solar radiation variables were introduced as the most effective factors in estimating the evaporation rate from class A pan.

Evaporation is one of the important processes that cause the losses of half of precipitation in arid and semi- arid regions. Accordingly, knowledge of the amount of evaporation and its modeling as one of the most important hydrological variables in agricultural research and factors related to water and soil of great importance. So, accurate estimation of this phenomenon is essential. In this study, meteorological data from Tabriz station were utilized to assessment capability of machine learning methods. Evaporation values were estimated using five data mining methods including SVR, GPR, M5, RF and LR. Conclusively, the results of evaluation criteria indicated that GPR and SVR models using all variable of meteorological data performed more accurate than others. Finally, both of them are recommended to estimate the amount of evaporation from class A pan.

It is important to find a simple connection between physic-chemical parameters and indices due to complexity and difficulties of using some indices. This research was performed to determine the relationship between physicochemical parameters and water quality indices

In this research, dominant water type of Gamasyab and GharehSou rivers was first determined using radial diagram. Then, to classify the water quality of two rivers, 14 quality water indices together with national standards for drinking water 1053 and WHO were used. Linear regression was used to determine the relationships between quality indices and physicochemical parameters. Finally, for the accuracy of the results of linear regression, the obtained relationships were tested for two other water sources (river and Gorgan Bay).

Based on hydrochemical results, the predominant type of water in both rivers is Ca-HCO3. Based on diagrams and criteria related to drinking and agriculture, the water quality of both rivers is in the desired range. The results of Langelier, Ryznar, Larsson-Skold and Puckorius indices showed that water quality for the industrial sector was relatively corrosive. Also, the water quality at the study stations is suitable for livestock drinking. The results of linear regression between quality indices with physicochemical parameters of water and some ion ratios showed that the resulting relationships for calculating quality indices can be used for different water types from fresh to saline. Also, comparing the results of linear regression simulations with water quality indicators, indicates the high accuracy of the relationships in water classification for different uses.

Land surface temperature (LST) is a key boundary condition in many ground-based modeling schemes based on remote sensing. Previous literature has shown that LST products from satellite imagery can be used to detect land surface changes, including urbanization, deforestation and desertification, which can improve our ability to monitor surface changes continuously. The objective of the present study was to evaluate the results of DisTRAD and TsHARP thermal sharpening methods to downscale the spatial resolution of MODIS LST from 1000 m to 250 m.

The research method in the present article is applied in terms of purpose and based on correlation relations in terms of method of work.

The performance of DisTRAD and TsHARP thermal downscaling methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE) between the downscaled and original LSTs. Statistical analysis showed that the RMSE between the downscaled images of DisTRAD and TsHARP methods with the original LST (1000 m (terra)) for 3 May 2019 were found to be 1.77 ° C and 1.7 ° C, respectively, whereas the R2 were found to be about 53% and for 17 October 2019, the RMSE were found to be 2.44 ° C and 2.38 ° C respectively, whereas the R2 were found to be about 85%.

The study of the results of Terra and Aqua satellites generally shows the superiority of Terra satellite results over Aqua. The main reason could be the different passage times of the satellites from the study area. Since that changes in soil moisture and water body such as the Karun River are common sources of error, so the use of these methods is recommended only in areas without excessive changes in moisture.

Soil physicochemical properties are important variables in runoff production and soil loss. This research was conducted to find the most important soil properties affecting runoff and soil loss in rainfed wheat furrows under fallow condition. To this end, three rainfed landswith 15% slope gradient were selected in south west of Kermanshah province. In each land, furrows with 5 m in length and 30 cm in width were created using sowing set. Runoff and sediment were measured using simulated flows with a discharge of 0.5 and 1.5 lit.min-1 for 60 minutes in three replications. The results showed that in the two flow discharges (0.5 and 1.5 lit.min-1) runoff and soil loss are negatively correlated with clay, sand, soil permeability (If), organic matter and clay to silt ratio (p<0.01), while positive correlations were found between them and silt (p< 0.01) and bulk density (p< 0.05). The results of multiple linear regression analysis showed that runoff production and soil loss by a 0.5 lit.min-1 flow discharge can be estimated using using the clay to silt ratio, organic matter and soil permeability (If) with a R2 of 0.94 and 0.81, respectively. Runoff production and soil loss by a 1.5 lit.min-1 flow discharge can be estimated using soil organic matter and soil permeability (If) with a R2 of 0.90 and 0.94, respectively.

In hydrological studies, time series are observed as continuous or discrete. Groundwater level and rainfall can be considered as discrete time series. The most common way to measure the dependence between two variables in a discrete time series is to calculate the Pearson correlation coefficient (r). Pearson correlation test is a parametric test that quantitatively measures the linear relationship between variables. This coefficient is essentially a dimensionless index that describes the relationship between two variables numerically. The groundwater level is more or less influenced by rainfall, and this influence may be delayed for a variety of reasons. The process of comparing two time series in different time steps is called cross-correlation. In the cross-correlation analysis, the time-dependent relationship between the dependent and the independent variables is analyzed by computing the coefficients of cross-correlation for various time lags. Results are plotted on a graph called a cross-correlogram.Mashhad-Chenaran aquifer with an area of about 2527 km2 is the most important aquifer in Khorasan Razavi province. Unfortunately, so far in the Mashhad-Chenaran aquifer, the recharge lag time has not been calculated due to the very complex geological and hydrogeological conditions of the aquifer. In this study, an attempt has been made to calculate the groundwater recharge lag time.

In this study, 15 years (Sep. 2001 to Sep. 2016) data of monthly depth to water-table and rainfall have been used . There is 74 active observation well in Mashhad-Chenaran aquifer. Out of 74 wells, 31 well were selected based on geological and hydrogeological conditions. To calculate the rainfall at the observation wells, the daily rainfall data from rain gauge and evaporation stations (25 rain gauge stations and 9 evaporator stations) have been used. First, the cumulative daily rainfall at each station for one month (from 15 months to 15 months later) was calculated. Then, a monthly rainfall raster was prepared using ArcGIS.Finally, the rainfall at the observation well was extracted from the raster file.

The correlation coefficient between the groundwater level and rainfall was calculated for the 31 wells at two confidence levels (α = 0.05 and α = 0.1). The lag time was calculated based on the highest correlation coefficient for the two confidence levels. Results showed that the cross-correlation coefficient varied from at least 0.129 in the Tanglshour-Morgh Pardak observation well (very weak) to 0.495 in the Kalateh Sheikhha observation well (moderate). The coefficients of cross-correlation for various time lags were plotted on the cross-correlogram. In cross-correlogram, the month zero was equivalent to October and the month 11 was equivalent to September of the next year. It was observed that the trend of correlation coefficient followed the two specific patterns. In the first group, the water table usually reacts to rainfall after the second month. Then, the correlation coefficient gradually increased. The correlation coefficient reached its maximum in the fourth and fifth months and then decreased with a gentle slope. From the seventh month to the eleventh month the correlation coefficient has become negative. Although there was a significant relationship during these months, there was no cause-and-effect relationship between changes in the water table and rainfall. In the second group, the relationship between the groundwater level and rainfall was not significant at the 95% confidence level. This group includes Doghai observation wells, Qarachah, Shurcheh, Mochenan, Yekehlengeh, Chamgard, Ghahghahe, Tangleshour - Morgh Pardak, and Shorcheh. Changes in the correlation coefficient of these wells were very irregular and the relationship between rainfall and water table changes was probably influenced by other factors. The map of lag time showed that the spatial variations of the lag time completely followed the pattern of the Iso-depth map. In general, the lag time was a function of the depth to the water-table in the Mashhad-Chenaran aquifer. With increasing water depth, the lag time also increased. A closer look at the map showed that in the northern and southern margins of the first hydrogeological unit, the lag time was more than its center. In the northern and southern hydrogeological units, the lag time showed the greatest compliance with the groundwater depth. The amount of lag time from the northern margin of the aquifer to the south gradually increased and finally reached its maximum value in the Akhlamad, Torqabeh-Shandiz.

As discussed previously, the groundwater level was influenced by rainfall, and this influence may be delayed for a variety of reasons. In this study, the groundwater response to rainfall has been estimated from 31 observation wells by cross-correlation method in a period of 15 years (Sep. 2001 to Sep. 2016). The correlation test results showed that after about 2 to 3 months, the effect of rainfall was gradually observed on the groundwater level and the correlation coefficient at the confidence level α = 0.05 and α = 0.1 for 77 % and 97% of wells became meaningful, respectively. The minimum lag time was 2 months and the maximum was 7 months. In general, the estimated lag time was well matched to the groundwater depth and fully followed the Iso-depth map pattern. The amount of groundwater recharge throughout the Mashhad-Chenaran aquifer was mainly controlled by the unsaturated area properties such as thickness, material, etc. Changes in groundwater depth were the major factor affecting the lag time. It seems that with the start of rainfall in late October, groundwater recharge in most wells begin in mid-autumn and continues until late spring. Most of the groundwater recharge takes place in late winter. In summer, rainfall has a very small role in groundwater recharge. In this period, the uncontrolled extraction of water from the aquifer and consequently a sharp and continuous drop in groundwater level plays a major role in water table fluctuations.

The purpose of this study was to model biomass with soil parameters in Hir-Neur rangelands of Ardabil Province. Initially, considering the vegetation types and different classes of environmental factors, at the maximum vegetative growth stage, using one square meter plot, biomass was estimated by clipping and weighing method. For each transect, a soil sample was taken and transferred to the soil laboratory and the various parameters were measured by conventional methods. The relationship between soil factors and the rangeland biomass was analyzed and simulated using linear multiple regression. Among the measured soil factors, the Silt, EC, Ca, Ksoluble, OC, POC, pH, Mg, TNV, clay, P, and volumetric moisture had the highest effect and percentage of biomass forecast (p<0.01). The accuracy of the simulated maps was analyzed using RMSE criteria and for grasses, forbs, shrubs, and total biomass were equal to 0.81, 0.65, 0.34, and 0.46, respectively. The results of this study, not only point out the importance of soil factors on the biomass but also as a baseline data for managing rangelands, supply-demand, and carbon balance can be used in the current section.

In the discussion of water demand management, it is important to have a predictive model of water consumption for the coming days. Such model can be useful in taking management decisions such as water rationing policies, water removal rates from wells and suitable timing for pumping water. Predicting water consumption in urban areas is of key importance for water supply management. Predictive modeling for water consumption can be used for planning water supply and expanding infrastructure for new developments and improving the control and operation of the water resources systems. In this research, the performance of Multi Linear Regression (MLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), coupled Wavelet and MLR (WR) and coupled Wavelet and ANFIS (WANFIS) were evaluated in predicting water demand in Kerman City, Iran. For this purpose, weekly time series of water consumption and meteorological parameters including maximum temperature and total precipitation were used to predict weekly water consumption based on 12 years data from 2006 to 2017. The data from 2006 to 2014 (469 weeks) were considered for the training of MLR, WR, ANFIS and WANFIS models and the remaining data from 2015 to 2017 (157 weeks) were used for the validation of various mentioned models. In WR and WANFIS wavelet-based models, the weekly time series of water consumption, maximum temperature and precipitation are decomposed by discrete wavelet transformation (DWT) to sub-series of approximations and details at various levels which are used as inputs of wavelet based models. The objective of multiple linear regression (MLR) analysis is to study the relationship between several independent or predictor variables and a dependent or criterion variable. The aim of this method is to determine the regression parameters by which the estimated values are efficient and consistent. Coupled Wavelet and Multi Linear Regression (WR) models are MLR models which use, as inputs, subseries components which are derived from the use of the Discrete Wavelet Transform (DWT) on the original time series data. Fuzzy Inference System (FIS) is a rule based system consisting of three components: (i) a rule-base, containing fuzzy if-then rules; (ii) a data-base, defining the membership functions (MF); and (iii) an inference system that combines the fuzzy rules and produces the system results. Fuzzy Logic (FL) is employed to describe human thinking and reasoning in a mathematical framework. The main problem with fuzzy logic is that there is no systematic procedure to define the membership function parameters. The construction of the fuzzy rule necessitates the definition of premises and consequences as fuzzy sets. On the other hand, an ANN has the ability to learn from input and output pairs and adapt to it in an interactive manner. In recent years, the ANFIS method, which integrates ANN and FL methods, has been developed. ANFIS has the potential benefits of both these methods in a single framework. ANFIS eliminates the basic problem in fuzzy system design, defining the membership function parameters and design of fuzzy if-then rules, by effectively using the learning capability of ANN for automatic fuzzy rule generation and parameter optimization. Coupled wavelet and Adaptive Neuro-Fuzzy Inference System (WANFIS) models are ANFIS models which use, as inputs, subseries components which are derived from the use of the Discrete Wavelet Transform (DWT) on the original time series data. In this comparative study, the performance of all predictive models was evaluated by statistical indices including coefficient of correlation (R), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The obtained results from this study suggest that the wavelet-based models including the WR model (for training: R2 = 0.92, RMSE = 34151 m3, MAE = 23908 m3 and for simulation R = 0.97, RMSE = 23486 m3, MAE = 16788 m3) and the WANFIS (for training: R2 = 0.94 RMSE = 29179 m3, MAE = 20675 m3 and for simulation: R = 0.92, RMSE = 43698 m3, MAE = 29305 m3) have much higher performance compared to the MLR and ANFIS models. By the results, it can be concluded that the best models for predicting weakly water consumption in Kerman City are those with the imputes of water consumption, maximum temperature, and total precipitation of last two weeks and data decomposition level of 3 via discrete wavelet transformation method.

The occurrence of wind erosion and the spread of dust particles can be regarded as one of the most important and threatening environmental factors. Climate change and the frequency of droughts have played an important role in exacerbating or weakening these events. The primary objective of the present study was to investigate the trend of changes in four important climatic elements (precipitation, temperature, wind speed and relative humidity) and dust storm index (DSI) in Qazvin city using the Mann-Kendall pre-whitened test and to determine the relationship between them based on the multiple linear regression method. Assessment of the meteorological drought status based on two standardized precipitation index and standardized precipitation, as well as the evapotranspiration index and analysis of their effect on activity level of dust events, was the other objective of this study in the study area. For this purpose, after preparing and processing the climatic data and calculating the dust storm index, the trend of changes and the relationship between climatic parameters and dust events were investigated. The results showed that the changes of trend in the annual precipitation and relative humidity in Qazvin city were increasing, while the trend of annual changes in the wind speed and the mean air temperature was a decreasing one. Investigation of the monthly changes in the dust events also showed that there was a sharp decrease in the occurrence of wind erosion and the spread of domestic dust particles only in July. On a seasonal scale, with the exception of winter that has been reported without trends, in other seasons, the intensity of these events was significantly reduced. The effect of the meteorological drought on wind erosion was estimated to be 11% at the confidence level of 99%. In general, these findings indicate a decreasing trend of land degradation and desertification caused by wind erosion in Qazvin.

Over application of phosphorous-containing fertilizers is common among the farmers. Excess amounts of phosphorus can potentially cause more phosphorous losses through water flow on the soil surface or leaching into the soil profile. The ability of highly phosphorus-fertilized soils to maintain excessive amounts of phosphorus and prevent losses largely depends on the phosphorus adsorption capacity of the soil. The purpose of this study was to investigate and compare phosphorous adsorption isotherms in soil samples of four agricultural areas located in Qazvin plain and determine the most appropriate equation to describe the equilibrium adsorption in the studied samples. Identification of the most accurate model of adsorption kinetics using the investigated kinetics equations in one of the soil samples was another objective of this study. The linear regression analysis and correlation between physical and chemical properties of different soils with adsorption coefficients of Langmuir equation was also investigated. Based on mentioned points, the results of this study can help to increase the availability of applied phosphorous for plants, reduce phosphorous losses and proper management of phosphate fertilizers consumption in the study areas.

In order to study the soil properties and phosphorous adsorption, soil samples of four villages included Zaaferan (A), Koochar (B), Mehdi Abad (C) and Kamal Abad (D) were taken from 0 to 30 cm depth and stored in plastic bags after air drying. Batch experiments using a standard method recommended by the SERA-IEG17 group were used to determine the amount of phosphorous adsorbed to soil particles. The steps to perform the equilibrium were as follows: 1- Dry soil samples were crushed and passed through a 2 mm sieve. 2- One gram of the soil sample was placed in a 60 ml container. 3- 0.01 M CaCl2 solution was prepared and different concentrations of phosphorous including 0, 5, 10, 15, 20, 30 and 80 mg/l were created by adding certain amounts of KH2PO4 to this solution. 25 ml of these solutions were added to each soli sample to give a 1:25 soil to solution ratio and three drops of chloroform were added to each container to prevent microbial activity. 4- The suspension samples were placed in a shaker machine (250 rpm) at 25°C for 24 hours. 5- Then, the samples were removed from the shaker and allowed to settle for one hour and then passes through a fine filter (Mesh 42). 6- Phosphorous concentration was measured by the molybdate-vanadate method followed by spectrophotometric determination at 470 nm. 7- The amount of phosphorous adsorbed in each soil sample was calculated from the difference of the initial and secondary concentration values. The adsorption kinetics experiment was similarly performed, with the exception that one soil sample with average adsorption value (sample C) was selected and the phosphorous solution at a concentration of 20 mg/l added to the soil samples. Phosphorous contact times with soil were considered as 0.17, 0.5, 1, 2, 4, 8, 16, 24, 48 and 72 hours. In this study, using CurveExpert 1.4 software and by matching Pseudo-first-order, Pseudo-second-order, Intra-particle diffusion, Kuo and Lotse (1974), Barrow and Shaw (1975) and Panda et al. (1978), equations on the data obtained from kinetics adsorption experiments, and the coefficients were estimated in these equations (adsorption parameters). Furthermore, by calculating the coefficient of determination (R2) of these equations and the standard error of the estimate (s), the most appropriate and accurate model of phosphorous adsorption kinetics for the soil sample was determined. Similarly, from Langmuir, Freundlich, Linear and Van Huay equations, the most appropriate isotherm was determined for estimating phosphorous equilibrium adsorption in the studied areas. Also, correlation and linear regression analysis were performed to determine the relationship between the physical and chemical parameters of the soils and the coefficients of Langmuir isotherm using Minitab software.

According to the results, the highest coefficient of determination (R2) and the lowest standard error of the estimate (s) for all four samples were related to Langmuir, Freundlich, Van Huay and Linear equations, respectively. Therefore, in this study, Langmuir isotherm was the most accurate model for estimating equilibrium adsorption of the phosphorus to the soils of the study areas. However, the Freundlich and Van Huay equations also showed a good correlation with the laboratory data. Comparison of the results of various studies in these fields showed that the type of isotherm corresponds to phosphorous adsorption data in each experiment is related to the physical and chemical properties of soil and adsorption sites. The amounts of maximum phosphorous adsorption capacity (qm coefficient in Langmuir equation) for the soil samples A, B, C and D were 0.49, 0.31, 0.42 and 0.4 mg/g, respectively. In kinetic study, Although, Kuo and Lotse, Barrow and Shaw and Panda et al. equations had a coefficient of determination (R2) above 0.95 ; the highest accuracy was related to the Kuo and Lotse equation with R2 of 0.974. The coefficients of this model included k (reaction rate) and m (constant coefficient) were 0.007 l/gr.min and 13.2, respectively. Based on the results of this study and other adsorption studies, soil physical and chemical properties including EC, PH, soil calcium content, clay content and porosity were among the parameters affecting adsorption rate and the type of the most accurate equation of adsorption estimation. Considering the soil properties that were most correlated with adsorption coefficients, it can be concluded that the high percentage of clay and low levels of organic matter and soluble calcium are the main causes of the high phosphorous adsorption in soil. The correlation coefficients (r) of these three soil parameters with the maximum adsorption capacity (qm) were 0.61, -0.97 and -0.92, respectively.

According to the results of this study, Langmuir was the most accurate isotherm model and the soil sample of Zaaferan area has the most adsorption capacity with qm of 0.49 mg/g, which is related to low levels of soil organic matter. Therefore adding organic matter to the soils can be used as a solution to increase cultivated plants access to applied phosphorous and reduce phosphorous adsorption into the soil and thus reduce losses and leaching of excess phosphorous in the profile or soil surface.

One of the most important natural hazards that is affecting a large number of people, due to extensive damage, is drought. Drought is also a phenomenon directly related to the issue of water scarcity and because of its reversibility, it can affect the various aspects of human life and the environment. The drought affects almost all the determinants of the hydrological cycle from the onset of precipitation and then the surface water flow and eventually storage in groundwater. Therefore, in this research, due to the results of the stable process of precipitation and temperature in the Ardabil DareRood basin, SPI Index which relies on precipitation, with the RDI index which combines the parameters of potential evapotranspiration and precipitation, were compared and evaluated in different time intervals.

In this study, in order to identify drought and wet periods, it was used from monthly precipitation data, Minimum and maximum monthly temperature for seven synoptic stations, for a 30 years (1985-2014) in the Ardabil DareRood basin in the northwest of Iran. In order to identify the trend in the precipitation and temperature series in this research was used Man-Kendall (MK) and Sen's slope (Sen) models. Also, to assess the long-term stability of the trend in the time series was used the LOWESS curve (at a significant level of 5%). Also using Aridity index, four stations in the arid area and three stations in the semi-arid region were established. In order to calculate the RDI index, the values of potential evapotranspiration were use. The amount of PET in the RDI index using monthly temperature values, and was obtained by the Hargreaves method. Finally, SPI and RDI indices were compared in 3, 6, 9, and 12-month scales.

The results of the LOWESS curve showed that the annual temperature at all stations follows an incremental scenario. While the precipitation behaves differently and often is decreasing. The results also showed that SPI and RDI indices are very similar in different time scales, and R2 is in most cases greater than 0.90. The extreme drought the observed in SPI and RDI models is related to the Ardabil station in 2010-2011 on a 3-month scale, whose values were respectively -3/11 and -3/09. Also, the results showed that the RDI index that Extreme and severe wet values it’s larger than SPI. Eventually, both indices are many similarities with each other but because of RDI's use of PET, It can be used more widely for arid and semi-arid regions of Iran

this research, two SPI and RDI indices were used to compare the drought events in the DarehRood Ardabil basin of the northwest of Iran.Due to the dry and semi-arid climate of most regions of Iran, It is very probable that the amount of precipitation is zero in some seasons. Therefore precipitation-based indices such as SPI may have less efficiency than RDI index, which, in addition to rainfall, uses potential evapotranspiration (PET) in their formulation. Considering the importance of PET parameter in agriculture and water resources management in Iran, It is necessary to examine the RDI index in other regions of Iran, such as the northwest and mountainous regions of Iran and its results can be compared with other indicators such as the important and highly applicable SPI indicator.

Awareness of the important moisture points is crucial for irrigation studies on the farm, but measuring this information in a direct way is very costly and time consuming. Therefore, several models and relationships have been developed as Pedotransfer functions which indirectly predict the hydrological properties of the soil using readily available soil data with the aid of a series of proper mathematical relationships (Nguyen et al., 2015). Since the measurement of important moisture points is a time consuming, costly and difficult work, many attempts have been made in order to use simpler soil properties such as texture, the amount of organic matter, and bulk density. Pedotransfer functions are indeed predictive functions which establish relationship between the soil’s readily available and latency data (e.g., the percentage of sand, silt and clay, bulk density and organic matter) including the parameters of the moisture curve (field capacity and permanent wilting point) (Botulla et al., 2013). Moreover, the functions that can be successfully implemented in an area may not have suitable adaptations in another area with real values. There are several methods for obtaining Pedotransfer functions, among them  are linear regression (LR), artificial neural networks, fuzzy adaptive-neural inference, and support regression vector.  Various researchers have studied the development of Pedotransfer functions and evaluated the predictive models in the water and soil sciences. As a sample, Shop and Lajj (1998) estimated the soil moisture curve using the neural network. They found that the artificial neural network was better than some of the regression Pedotransfer functions provided by other researchers, and if more readily available properties were used as inputs, the prediction accuracy increased. However, there was always a significant difference between the predicted and measured moisture values. Zhang et al. (2007) estimated the soil moisture curve for 110  non-calcareous soil samples with different tissue classes through the artificial neural networks and regression models. They showed that the neural network predicts the moisture curve better than the regression method with higher correlation coefficient in most tissue classes. Lin et al. (2009) argued that the SVM method was much faster trained than the artificial neural network. SVM was also found to have a more accurate prediction than the artificial neural network. Chen et al. (2010) used support vector machines to model daily rainfall and compared the results with that of the multivariate analysis method. It was found that the results of predictions from SVM were more accurate. In turn, Kaihua et al. (2014) used support vector machines to predict cationic capacity on different horizons of the soil in Qingdao, China. They performed their studies at 208 points on two horizons of the soil, and concluded that the SVM model improved predictions. Considering the significance of knowing the important points of soil moisture in Shahrood area for agricultural projects and irrigation schedules, developing appropriate Pedotransfer functions and evaluating models is necessary so as to obtain moisture of the field capacity and permanent wilting point. This research, thus, evaluates the performance of three models of support vector regression, artificial neural networks, and linear regression in the development of soil Pedotransfer functions and the effect of number and type of input variables on the performance of the models.

Rainfall variations is one of the most important factors in climate change which can be analysed using different methods. This research aims to investigate the application of the innovative-Sen method for monthly, seasonal, and annual trend analysis of precipitation in Khorasan Razavi province. A series of 30 years of rainfall data from 39 rain stations of the province have been used. For the comparison purpose, Mann-Kendall, Spearman, and linear regression methods have also been considered. For quantitative evaluation of graphical results of the Sen method, a statistical analysis has been proposed. The results of traditional methods indicated that the rainfall of Aban month )October( has a significant increasing trend in confidence interval of 95%, and the rainfall of other months, seasons, and years do not show any significant trend, except for the regression method. Rainfalls of Aban )October(, Khordad )May(, and Tir )June( months, as well as autumn and summer seasons, have an increasing trend and other considered periods have a decreasing trend. The results of innovative-Sen method analysis in low, medium, and high values indicated that the maximum and minimum variations are related to the rainfall of winter and Mehr )September( month, respectively. Also, the statistical analysis showed a significant trend similar to the regression method only exist in the winter season of the province.

Soil salinity is a serious environmental problem especially in arid and semiarid areas. Therefore, it is vital to generate and update soil salinity maps in order to determine early stage of salinization. Electromagnetic induction instrument is an alternative to traditional methods for assessing soil salinity. Different methods have been used to calibrate electromagnetic induction instrument. At present research, an attempt was made to calibrate EM38 in pistachio orchard located in Ardakan city using multi-linear regression (MLR), artificial neural network (ANN) and neuro-fuzzy (ANFIS). To calibrate and predict soil salinity in nine standard depths, the best result was obtained by ANFIS model with R2 of 0.06, 0.11, 0.30, 0.59, 0.69, 0.64, 0.70, 0.74 and 0.74; and RMSE of 4.09, 3.66, 2.87, 2.22, 2.26, 2.62, 2.46, 2.38 and 2.50, respectively; which showed the accuracy of ANFIS was higher than other models (ANN and MLR) to predict soil salinity and calibrate EM38.

Introduction The water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN), Wavelet Neural Network combination (WANN) and multi linear regression (MLR) models, to predict next month the Nitrate (NO3) and Chloride (CL) ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated.
Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL) has been put related to the past amounts of the same time series (NO3 / CL) and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data) as training set, next 10% of data (12 months) and 10% of the end of time series (terminal 12 months) were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q) and mentioned qualityparameters(NO3 / CL) were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E) and the Root Mean Square Error (RMSE).An efficiency of one corresponds to an accurate match of forecasted data to the observed data. RMSE indicates the discrepancy between the observed and predicted values
Results Discussion The results indicates that the accuracy and the ability of hybrid model of wavelet neural network had been better than the other two modes; so that hybrid model of Wavelet artificial neural network was able the improve the rate of RMSE for Nitrate ions in comparison with ANN and MLR models respectively, amounting to 30.13% and 71.89%, for chloride ion as much as 31.3% and 57.1%. In the WANN model increasing the decomposition level, in level 1 to Level 3, increases the model’s performance, but increasing the decomposition level, in levels over Level 3, decreases the model’s efficiency, because high decomposition levels lead to a large number of parameters with complex nonlinear relationships in the ANN technique.The WANN model needed 1 to 7 neurons in the hidden layer for the best performance result. In prediction of high NO3 values the amount RMSE for ANN, MLR and WANN models are 1.487, 2.645 and 0.834 ppm, respectively. Also, for CL values the mentioned statistical parameter is 0.990, 3.003 and 0.188 ppm, respectively for models.The results exhibits that the combined model of WANN the forecast was better than the other two models.
Conclusion Wavelet transforms provide useful decompositions of original time series, so that wavelet-transformed data improve the ability of a predicting model by capturing useful information on various resolution levels. The main advantage of this study is that only from the Q and slightly quality of parameter time series are used until the same quality of parameter in one month ahead is predicted. The purpose of entering Q time series with quality of parameter as inputs of models is analysis the efficacy of Q in the accuracy of prediction. owing of the high capability wavelet neural network in the prediction of quality parameters of river's water, this model can be convenient and fast way to be proposed for management of water quality resources and assurance from water quality monitoring results and reduction its costs.

Morphology of rivers constantly changes due to their dynamic features, and these changes can negatively affect the structures in riversides, agricultural lands, etc. Meanders are considered as important indicators of environmental change. This study examines the changes of the sealing degree and meander shapes of the Kashafroud river using 1966 aerial photography, 1987 Landsat TM sensing images, and 2013 Google Earth images. In this study, morphological parameters of 16 meanders in a range of 11 kilometers along the Kashafroud river was determined for each period. The results of the paired t mean comparison showed that changes in the sealing degree ranging from 1966 to 2013 have a significant difference at level of one percent. To determine the effects of changes in some meanders parameters on sealing degree, multivariate linear regression model was used. The results showed that in case of change in the sealing degree, meanders reduce along the river. In addition, Spearman correlation analysis showed that changes in the parameters of sealing level and meander shape are strongly interdependent, so that there is a significant correlation between changes in sealing degree and meander shape at level of one percent. With increasing degree of sealing, meander shapes convert into U and Ω shapes, and meanders tend to close. In meanders with reduced sealing degree, such as meanders number 2, 3, 11, and 12, the path will mainly change into a strait one, and a part of the river will be separated as bovine horn ponds.