multiple linear regression

As the global demand for water resources increases, the reduction in water loss, including Evapotranspiration, becomes more obvious. Although many models have been developed to predict evapotranspiration, no universally accepted model for all climate regions has been established. Several soft computational models have been created to circumvent the constraints of empirical models and accurately predict ET. Soft computing models typically require less data and are applicable across various climatic zones. This study aimed to analyze how well two Random Forest models and Multiple Linear Regression could predict ETo in the Ardabil plain region. Meteorological data from the Iranian Meteorological Organization were used to calculate the reference evapotranspiration from 2014 to 2016. In constructing the model, data from 4 meteorological stations were combined to generate a random time series, while the fifth station was reserved for evaluating the models. The assessment metrics used comprised RMSE, R2, and NSE. The RF model achieved higher accuracy with R2, NSE, and RMSE values of 0.74, 0.743, and 8.20 mm, respectively, compared to the MLR model. The results demonstrate that random forest models are reliable tools for forecasting ETo with minimal climate data. In general, using the results of this study and other similar research, we conclude that RF and MLR models simulate potential evapotranspiration with acceptable accuracy but are sensitive to the number of input parameters.

Wildfires represent a significant environmental challenge globally, with climate change exacerbating their frequency and severity in many regions. In the Iranian Northern Zagros Forests, a Mediterranean climate prevails, characterized by hot, dry summers and relatively mild winters, rendering these forests particularly susceptible to fire. Understanding the relationship between climate change and wildfires in this region is essential for informing effective management strategies and mitigating associated risks. This study aims to investigate the effects of climate change on wildfires in the Iranian Northern Zagros Forests, focusing on changes in fire frequency and extent over a 17-year period from 2006 to 2023.

To achieve this objective, we employed a combination of statistical analyses, including Pearson's correlation and multiple linear regression (MLR), using climate data and historical wildfire records. We analyzed the relationship between the number and extent of fires and climatic variables such as average annual temperature and relative humidity. Pearson's correlation was utilized to assess the strength and direction of the linear relationship between these variables, while MLR was employed to predict the burned area based on temperature and humidity.

From 2006 to 2023, the forests of Sardasht experienced a significant number of fires, with the most occurring in the months of August, July, September, and June. The greatest scorched area of 211.62 hectares was reported in 1402, while the most frequent fires were recorded in 1400. This tendency is likely to continue. The study found a positive and significant relationship at the 95 percent confidence level between the number of fires and the burned area in Sardasht forests and the average annual temperature, but no significant relationship was found between other climate data and fire factors. The results of the MLR analysis demonstrated the predictive power of temperature and humidity in determining the burned area, highlighting their significance as drivers of wildfire activity in the region.

In conclusion, our findings underscore the escalating threat of wildfires in the Iranian Northern Zagros Forests driven by climate change-induced changes in temperature and humidity. The observed increase in fire frequency and extent highlights the urgent need for proactive measures to mitigate fire risks and enhance resilience in fire-prone ecosystems. By integrating scientific research with stakeholder engagement and policy action, we can develop effective wildfire management strategies that balance ecological conservation with socio-economic priorities, thereby ensuring the long-term viability of forest ecosystems and the communities that depend on them.

Life on Earth is influenced by precipitation. Precipitation is one of the most significant factors that affect the hydrological cycle. Considering that precipitation is non-linear, complex, and can be changed according to spatial and temporal, estimating the amount of this important atmospheric factor in each month or year for each region and watershed is particularly important in managing and optimizing water resources. Various optimization models and algorithms have been proposed for modeling hydrological systems in recent decades. These algorithms have significantly reduced errors and increased accuracy. Still, since hydrological systems rely on random events, none of the methods can be completely and accurately selected as a superior model for modeling and estimating. The honey badger algorithm is an innovative algorithm that requires a few iterations to achieve an optimal solution, and this increases the speed of reaching the desired results. In current study investigates the performance of three models, including multiple linear regression (MLR), artificial neural network (ANN), and hybrid artificial neural network with honey badger optimization algorithm (HBA-ANN) for modeling the temporal and spatial precipitation in East Azarbaijan province. The best-developed model was selected by evaluation criteria such as R, RMSE, NRMSE, MBE, and NSE, the best model is selected.

The MLR model is one of the methods to analyze and investigate several variables. In this method, the model has one dependent variable and several independent variables, so that a linear equation is generated between the independent variables called X1, X2, ..., Xn and the dependent variable Y. ANN is a black box model of neural networks in the human brain. One of the most used methods is the BP method, which includes two stages. In the first stage, which is entitled feed-forward, the error value is calculated, after comparing output and objective values. In the second stage, which is labeled the back-propagation, the error value calculated in the previous step is corrected. The mentioned two stages continue until the output of the model approaches the desired output. The HBA is a new algorithm that simulates the honey-seeking behavior of a creature called the honey badger. The HBA includes two stages. In the first phase, the locations of this creature are calculated, and in the second phase, the exact distance between the HBA and the prey (dj) is calculated based on the honey intensity (S) and the honey smell intensity (Ij), as well as its new and optimal location for the prey Xnew. In the HBA-ANN model, the HBA algorithm is used to determine the most optimal output value in the ANN and increase performance in modeling. Therefore, the developed hybrid model can have the characteristics of both ANN and HBA methods.

In this study, in the first stage, the temporal modeling, and in the second stage, the spatial modeling of the monthly precipitation of 18 stations in East Azarbaijan province during the period of 1996-2022 using MLP, ANN, and HBA-ANN models has been paid. For temporal modeling of precipitation, one and two-month precipitation delay steps of the stations were used as input parameters. The first 70% of the dataset was selected for the training phase and the last 30% of the dataset was selected for the testing phase. Based on the results obtained from evaluation criteria and graphic diagrams, it can be concluded that the HBA-ANN model indicated significant accuracy compared to other models in the temporal modeling of precipitation. Also, by comparing the results of the stations in the HBA-ANN model, the Heris station with R =0.94, RMSE=2.25, NSE=0.79, NRMSE=0.04, and MBE=1.06 in the testing stage performed better compared with other stations. For spatial modeling of precipitation, the geographic coordinates of the stations, which include longitude, latitude, and altitude, are used as input parameters, and average monthly precipitation is used as the output parameter. From eighteen stations, 70% of the stations were selected for the training phase and 30% of the stations were selected for the testing phase. Based on the results obtained from R=0.95, RMSE=1.03, NSE =0.92, NRMSE = 0.03, and MBE = -0.81 and graphical diagrams, it can be concluded that the HBA-ANN model revealed significant accuracy compared to other models in spatial modeling of precipitation.

Precipitation is one of the most important factors that significantly change the hydrological cycle. Therefore, modeling and estimating this parameter is vital. In this study, the performance of multiple linear regression (MLR), artificial neural network (ANN), and hybrid ANN using honey badger algorithm (HBA-ANN) models were used for the spatial and temporal modeling of precipitation in East Azarbaijan province. For spatial modeling, the time delay steps of one and two months of station precipitation were selected as input parameters. Also, for temporal modeling, the longitude, latitude, and altitude parameters were used. The mentioned models were evaluated by R, RMSE, NSE, NRMSE, and MBE assessment criteria. According to the results of temporal modeling, the HBA-ANN model for all stations, especially Heris station with R equal to 0.94, RMSE equal to 2.25, NSE equal to 0.79, NRMSE equal to 0.04, and MBEequal to 1.06 is selected as the superior model. Also, based on the results obtained from spatial modeling, the HBA-ANN model with R equal to 0.95, RMSE equal to 1.03, NSE equal to 0.92, NRMSE equal to 0.03, and MBEequal to -0.81 was selected as the best model. The MLR and ANN models, respectively, presented a relatively poor performance compared to the developed hybrid model.

Artificial intelligence models as powerful methods in modeling nonlinear complex problems, have a significant ability and this has been proven in numerous articles. Artificial intelligence has been used in various issues, including engineering, medicine, etc. The success of this method in comparison with analytical and numerical methods, their easiness, speed and accuracy caused to open their place among researchers as much as possible. Today, Considering that one of the challenges of human life is the issues related to water resources management, so in this study, an attempt has been made to investigate the performance of artificial intelligence and regression models in the cases of water resources. Various researches have been done in the case of modeling and parametric analysis of water resources. However, in this study, artificial intelligence (Learning Machine) models were used to simulate the qualitative and quantitative parameters of water. The models used in this study are: Self-Adapting Extreme Learning Machine (SAELM), Least Square Support Vector Machine (LSSVR), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) model which was used to predict changes in hydrogeological parameters. Today, due to the growing global population, one of the most important challenges is access to safe drinking water. In our country, Iran, due to its location in the semi-arid region and low rainfall, this danger is felt more than ever. One of the serious issues is the salinity leakage into groundwater resources. In this study, an attempt has been made to simulate the leakage of salinity dynamic flow into the freshwater resources of the coastal aquifer, using artificial intelligence and statistical models. At the end, the simulation results and the accuracy of the models are given. The study area in this work, is Mighan Wetland and Mighan aquifer in Markazi province. Annual rainfall occurs in small amounts in this area. According to the statistical results provided by synoptic and rain gauge stations in the region, the maximum and minimum rainfall values range from 461 mm in the northeast to 208 mm in the center of Arak plain. The hydraulic outlet of the aquifer to the Mighan plain is located in the center of the plain. The water entering the Mighan plain and leaves the system due to evaporation from the water table. Observatory wells were used to sampling this lake due to its saline water. The wells were located in an area called Vismeh near the lake.

In this study, qualitative and quantitative parameters: water salinity, total dissolved solids (TDS), chlorine ion (cl), sampling time (t), electrical conductivity (EC), Salinity and groundwater level (GWL) were simulated. In this work, Adaptive Neuro Fuzzy Inference System (ANFIS), Least square support vector machine (LSSVM), Self Adaptive Extreme learning machine (SAELM) and Multiple linear regression (MLR) models were used for simulation. In this study, data from 173 months of sampling were used. 70% of the sample size was used for training and 30% for testing models.

Simulation was performed using artificial intelligence models and regression model. The simulation results showed higher accuracy of artificial intelligence models. After simulation and obtaining the results, then the uncertainty analysis was performed by Wilson Score method without continuity correction. In this method, the prediction error (ei), the mean prediction error (Mean) and the standard deviation of the error is (Se). If the mean error value of a model in predicting the target variable is positive, it means that the performance of the model is Over Estimated. Also, if the average value of the model error is negative, the performance of the model is Under Estimated. Moreover, the results of Uncertainty Analysis with a significance of 5% were obtained. and finally we briefly write the subsequent performance Over Estimated (OS) and Under Estimated (US).

The results showed that different models were successful in predicting water parameters. In order to comprehensively evaluate the accuracy of the models in the simulation, the performance of the models was measured by five approaches. The proposed approaches were: 1) Evaluation of prediction by accuracy chart, 2) Performance evaluation by mathematical indices, 3) Performance evaluation, by Uncertainty Analysis by Wilson Score method without continuity correction, 4) Accuracy evaluation by error distribution charts and 5) Performance evaluation by discrepancy rate (DR) charts. Finally, all the results are given at the end of each section, respectively.Approach 1- According to the prediction accuracy charts, 16 charts were drawn and the most accurate models of which are depicted in Figures 4 to 7. After modeling, the results showed that the most accurate models in simulating groundwater parameters were SAELM model in GWL simulation. According to the results, SAELM model in GWL and EC simulation, LSSVM in TDS simulation and MLR in Salinity simulation were the superior midel, Respectively.Approach 2- According to the performance measurement indices, finally the results showed that SAELM model was the best model in simulating parameters (EC) and (GWL). The LSSVM model was also the most accurate model in modeling (TDS). MLR model was the best model in (Salinity) parameter simulation.Approach 3- Uncertainty analysis was performed based on Wilson score method. The performance of the models in the simulation showed that the performance of the SAELM model was determined as Under estimated and other superior models in simulation had Over estimated performance.Approach 4- Based on the error distribution diagrams, the best accuracy was assigned to SAELM and MLR models.Approach 5- Based on the discrepancy ratio, SAELM and MLR models were estimated to be the most accurate models in the simulation.

As a sustainable forest indicator, biodiversity plays a crucial role in understanding the effects of climate change on forest ecosystems. Measuring the diversity of trees and shrubs in forests is essential for monitoring and evaluating changes in biodiversity. Remote sensing (RS) is an effective tool for collecting such data. To estimate tree and shrub species diversity, we used Sentinel-2 data from August 10 and October 13, 2021. We measured 75 field plots with dimensions of 20 m × 20 m in the Patom, Namkhaneh, and Gorazban districts. In each field plot, the tree species and diameter at breast height of all trees with a diameter greater than 7.5 cm were recorded. We used the Jaccard and Sorensen indices in R software to calculate the beta diversity indices for each sample plot. Preprocessing steps were applied to the Sentinel2 data, and we then performed several spectral transformation approaches, that is, vegetation indices (VIs), principal component analysis (PCA), and Tasseled Cap, and generated texture variables. A vector map was used to extract the spectral and textural values corresponding to each field plot. Correlation analysis between the measured species diversity and spectral and textural variables was conducted at a 95% probability level. Multiple Linear Regression (MLR) analysis was performed using stepwise and Random Forest (RF) methods for modeling. Our regression analysis revealed that texture variables with a window size of 5×5 and spatial resolution of 10 m in Sentinel-2 summer images had the best performance in estimating the Sorensen diversity index( R2= 0.383 and RMSE%= 36.57). However, based on our results, we can conclude that the Sentinel-2 data has a moderate performance in estimating diversity in the Patom, Namkhaneh, and Gorazbon districts.

In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, they have increased the depth of wells to access water. In some areas, the groundwater depth (GWD) is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the GWD to provide a regional model with multiple linear regression (MLR) method for Arak plain aquifer. For this purpose, the average GWD in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources  are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GWD map for the region was created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root mean square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GWD drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values ​​at the maximum operating radius of the well, and the distance from water resources are the main factors of GWD drawdown. But the effectiveness of ANN in estimating GWD drawdown is higher than the MLR method.

Water infiltration to soil play an important role in irrigation management, storage moisture in soil especially in dry and semi-dry area and increasing agronomy yield. Understanding water infiltration to soil is of important in designing and applying water conservation methods, flood and runoff control and soil erosion management. Additionally, accurately measuring water infiltration to soil in different times is more important for predicting water storage in root zone, irrigation designing and planning and agronomy management. In other hand, water infiltration to soil is a base for precision agriculture, therefore, producing accurate maps for water infiltration to soil play an important role in land management and applying precision agriculture. Modeling soil water infiltration at the field scale with ruler of calcareous, saline and sodic conditions is important for a better understanding of infiltration processes in these soils and future of infiltration modeling. The present study aimed for estimating water infiltration to soil at different times using soil spatial prediction functions and spatial estimators.

in present study, 72 soil samples were collected using a random sampling method in Marvdasht plain, Fars Province. In selected points, soil bulk density, sand silt, clay, pH, electrical conductivity, calcium carbonate, solution sodium, solution calcium and magnesium and organic carbon contents. For measuring water infiltration to soil, the double ring method were used. Multiple linear regression (MLR), artificial neural network (ANN) and spatial estimators were used for deriving soil spatial prediction functions models between water infiltration to soil in different times including 5, 10, 20, 45, 90, 150, 210 and 270 min. In this study, the readily available soil properties and auxiliary variables such as remote sensing and topography data were used in soil spatial prediction functions.

The results of evaluating regression and artificial neural networks models based on the mean error (ME), coefficient of determination (R2) and root mean square error (RMSE) criteria in testing phase were showed that the developed artificial neural network models in present study performed better than multiple linear regression models in water infiltration to soil prediction at different times. Moreover, the results showed that the combined estimators (artificial neural network - Kriging) performed better than ordinary kriging model for estimating water infiltration to soil.

In totally, the results of this study showed that the applying soil spatial prediction functions (using auxiliary variables such as remote sensing data and topography data with the readily available soil properties) had a great potential to predict spatial estimation of water infiltration to soil at most considered times.

In order to manage the forests and optimal and sustainable utilization of the forest, it seems necessary to know the information on the volume of the residual stand. In this study, a systematic randomized inventory was carried out in 186 circular 10-acre plots in the educational and research forest of Darabkola, Sari, Golestan, Iran and the volume of each plot was obtained. In the next step, the physiographic layers of the area were prepared using the topographic map and the vegetation characteristics were prepared using the LISS-III image of the IRS-P6 satellite with a resolution of 23.5. After preparing physiographic layers and vegetation characteristics, their value was calculated for all plots. Then, regarding these variables and using two methods of multilayer perceptron neural network and multiple regression model, modeling was done. The results showed that the multiple linear model could model the volume changes in the region with higher accuracy (R2=0.75 and RMSE=0.3). The results of this research can be used in management planning and as one of the effective factors in the design of logging routes and forest roads so that areas with larger volumes are covered more.

Cation exchange capacity (CEC) is one of the most important soil chemical properties that plays an important role in soil fertility. However, standard laboratory methods for measuring CEC are difficult, costly, and time-consuming. The aim of this study was to use new methods such as 1) Pedotransfer Functions (PTF) based on the basic soil properties using Multiple Linear Regression (MLR), 2) soil spectroscopy (Vis–NIR, 400 – 2500 nm) using Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), for estimating soil CEC. Also, from the regression coefficient analysis, key wavelengths were introduced. For this purpose, CEC was measured using the sodium acetate method for 72 soil samples collected, and spectral reflection of soil samples was determined using spectroscopy. These methods are made from a calibration set (70% of data) and evaluated with a validation set (30% of data). The results showed that Vis - NIR method performed better than PTF. In this study, wavelength ranges around 566, 854, 1354, 1418, 1906, 2071, 2203, 2319, and 2341 nm were investigated as the key wavelengths for estimation of CEC. Furthermore, the results of prediction models showed that SVR has a better performance than PLSR. This study proved that Vis-NIR is a promising method for soil CEC estimation.

Quantitative and qualitative analysis of water resources has become one of the most widely used topics in water resources research today. In this research, data mining, artificial intelligence, mathematical techniques have been used to simulate water behavior and estimate its parameters changes. The models used to estimate hydrogeological parameters are Self-adaptive Extreme learning machine (SAELM), Least square support vector machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple linear regression (MLR) models. Also, to evaluate the performance of these models, the accuracy of the models was assessed in the form of 5 approaches. The results showed that the SAELM model was the best model based on the simulation and correlation diagrams. Based on accuracy evaluation indices, the SAELM model with RMSE, MAPE and, R indices equal to 0.1545, 0.0070, and 0.9979, respectively, had the highest accuracy in hydrogeological parameters prediction. Based on Uncertainty Analysis by the Wilson Score method, the performance of the top model (SAELM) was estimated to be underestimated. Also, based on the error ratio diagrams, the most accurate results were related to the SAELM model. Finally, the SAELM model was assigned the lowest error rate using the error distribution diagrams.

The humus index is a numerical score based on the visual assessment of organic horizons and humus forms identification. This index reflects different characteristics of forests stand and soil. Forest humus is an intermediate between plants and soil, which is affected by various factors, such as topography, climate, vegetation and soil. Nevertheless, by digging the humus profile and determining its index, it is also possible to determine many ecological factors. The aim of this study was to investigate the humus index and its relationship with topographic, soil and forest cover characteristics along an altitudinal gradient in the four-altitude class, including 0, 500, 1000 and 1500 meters above sea level (m a.s.l.), in Vaz forests located in Mazandaran province.

In each altitude class, the characteristics of the trees such as crown canopy, density, diameter and height, as well as topography and soil characteristics such as altitude, slope percentage, soil moisture and temperature were taken in 400 m2 plots, and in each plot, five 30 × 30 cm profiles for identification of humus forms were drilled and then identified and classified based on Humusica classification. In order to evaluate the humus index between different altitude classes was used one-way analysis of variance (ANOVA), as well as to analyze its relationship with the mentioned characteristics, multiple linear regression analysis (MLR) was used.

The results showed that the highest amount of humus index and litter thickness belonged to the altitude class 1500 m a.s.l. and their trend is increasing along the altitudinal gradient. Also, the results of MLR indicate the existence of a linear and significant relationship is between humus index and topographic and soil characteristics with high determination coefficient (R2 = 0.76), especially soil temperature with (P-value = 0.000), while the tree cover characteristics with a low determination coefficient (R2 = 0.31), cannot well be a good predictor for evaluating the humus index. Meanwhile, soil temperature and trees crown canopy are two factors that have shown the highest t-statistics in relation to the humus index, so that the decrease in soil temperature and trees crown canopy has increased the humus index.

In general, it can be concluded that with increasing altitude, the organic part of the forest floor and Humus index increase, and their variability is mostly due to changes in topographic and soil temperature characteristics.

Soil water Infiltration plays an important role in the water cycle of nature. However, since the direct measurement of soil water infiltration is laborious, time-consuming and expensive. Therefore, in this study was conducted to investigate the possibility of estimating the coefficients of water infiltration equations such as Kastiakov, Kastiakov-Lewis, Horton and USA Soil Conservation Service (SCS) using readily available soil properties and to evaluate the validity of these functions outside of their derivation regions. Therefore, soil physical properties and cumulative infiltration were measured in two different regions (T1 and T2). Parametric functions were derived using T1 location measurements and validation was performed using independent T2 measurements. Cumulative infiltration was measured using double rings with three replications at 78 points. Parametric functions were created using multiple linear regression. The highest accuracy of parametric functions in the derivation stage was related to SCS equations with the coefficient of explanation (R2) equal to 0.66 and the lowest accuracy with the value of R2 equal to 0.04 was related to the power of Horton equation. In the validation stage, the accuracy of the functions showed a large decrease. R2 changes range from 0.00 to 0.26, normalized root mean square error (nRMSE) range from 1.76 to 80.93 and geometric mean error ratio (GMER) range from 0.94 to 1.73 Was obtained. Therefore, in this study, the use of parametric functions to estimate the coefficients of infiltration equations outside of their derivation regions was not efficient.

Today, several modeling methods have been used to cost-efficiently predict the physical and mechanical properties of wood-based panel products which in turn reduce the cost of quality control of these products. Two common methods include regression and artificial neural network (ANN). In this study, the possibility of predicting the modulus of rupture (MOR) and modulus of elasticity (MOE) of particleboard by simple and multiple linear regression and ANN based on structural parameters including density in three levels (0.65, 0.7, and 0.75 g/cm3), slenderness ratio of particles in three levels (47, 30, and 13) adhesive percent in three level of (8, 9.5, and 11%) were evaluated. experimental and predicted data were compared with different criteria including mean absolute percentage error (MAPE), mean squared error (MSE) and coefficient of determination (R2). The results revealed that although both multiple linear regression models and artificial neural network were able to predict MOR and MOE values with acceptable accuracy, but ANN model predicted them with higher R2 and lower MAPE than multiple linear regression model. The value of MAPE and R2, for prediction of MOR and MOE by ANN model were 7.72% and 0.77, and 7% and 0.86, respectively. the corresponding value for multiple regression model were 8.3% and 0.738, and 9.06% and 0.783, respectively. These levels of error are industrially and practically satisfactory for the prediction of properties in particleboard.

Canopy cover is an important forest structure parameter with many applications in ecology, hydrology and forest management. This study aimed at investigating the capability of Landsat 8 satellite OLI data for modeling and estimation of forest canopy in a part of the Zagros forests. First the images were evaluated in terms of geometrical and radiometric errors. Required processing such as Vegetation Indices, Principle Component Analysis (PCA), and Tasseled Cap transformation were performed on the images of the study area. To measure aboveground canopy cover using hemispherical photography method, 60 sample plots were designed with a square dimension of 45 × 45 m. The spectral values of the corresponding sample plots were extracted using a polygon map of sample plots. Forest canopy cover was modeled using stepwise multiple linear regression and the accuracy of the model was evaluated via the k-fold cross validation technique. The results of multiple linear regression between canopy cover with main and computational bands showed that the model obtained from SR vegetation index and band 8 with R2 = 0.662 and RMSE (%) = 15.24 was the best model. Overall, this study demonstrated that the estimation of forest canopy cover is cost-effective and requires low computation power using Landsat 8 satellite.

Aggregate stability of soils informs about their relative strengths against erosive forces and mechanical disruption. In this research, a hybrid Genetic Algorithm-Artificial Neural Network method was used to select the best subset of features affecting the mean weight diameter (MWD. In addition, the ability of ANNs and multiple linear regression (MLR) for quantifying the relationship between the MWD index and some soil properties was assessed. After the modeling process, the importance of the selected features in relation to spatial variability of aggregate stability was investigated. In order to prepare a suitable data set; MWD index and some soil features were measured in collected soils from 90 sampling points. Feature selection results showed that six soil features including clay, sand, organic matter, calcium carbonate, electrical conductivity, and sodium adsorption ratio had the greatest effect on the aggregates stability of the studied soils. According to the MWD modeling results, the obtained values of coefficient of determination (R2), mean absolute error percentage (MAEP), and root mean square error (RMSE) for the ANN model performance were 0.94, 21.39, and 0.07% respectively. These findings indicated that the developed ANN model was able to predict the complex and nonlinear relationships between the MWD index and the soil properties selected by the algorithm. Based on the sensitivity analysis results, calcium carbonate equivalent, sand particles, and organic matter were identified as key factors in estimating aggregate stability. Overall, this study provides a robust framework for the prediction of aggregate stability and identifying the most determinant parameters influencing it in arid and semi-arid soils that could be applied to other regions with similar challenges.

Rainfed cultivation is one of the best ways to rich the sustainable development with optimal use of green water. About 75 percent of the world's cultivated areas are rainfed, which forms important part of the international economy. For a long time rainfed cultivation has been a common method in low-rainfall areas of Iran and mentioned method is one of the main ways of producing crops yet. Rainfall is an important factor in rainfed agriculture, so its limitations and non-conformity with planting season can reduce the production efficiency. Wheat growing period consists of three seasons of the year. Rainfall in spring, fall and winter is effective on wheat growth. Cereal grains, especially wheat, are the most important food source in the world. After cereals, legumes are considered to be the main human food source and have significant nutritional and agricultural importance. In addition to being high in quality and valuable, suitable and complementary protein for cereals in the nutritional pattern, lentils are also among the legumes that stabilize the air nitrogen in the soil, which lends itself to crop rotation. Thus, intermittent cultivation of rainfed wheat with rainfed lentil is an important factor in stabilizing production in the developing countries. Given that Iran is in arid and semi-arid regions and its population is growing, it is important to plan, and evaluate the past in order to predict the future. Zanjan province is the fifth country with 6.9% of total grains production. In this study, the effect of precipitation in water requirement of these two important products and their water stress has been studied in the climatic conditions of Zanjan plain. Zanjan province is located at the longitude of 47º 10' to 50 º 5' East and the latitude of 35º 25'37º10' North. The area of Zanjan province is about 39369 square kilometers. According to the De Marten climate classification, the tropical, temperate and subtropical climatic zones can be identified at the applied level. Average rainfall over the past decade has been reported as 301 mm. For conducting this study, statistical data including meteorological and crop yield data 2004-2014 obtained from the Meteorological and Agriculture-Jahad Organization, respectively. Zanjan synoptic station with the latitude and longitude of 48º 31' East and 36º 39' North, respectively, is located 1663 meters above the sea level. In the last 50 year, the annual precipitation trend has been decreasing and this trend is affecting the water availability of rainfed plants in the region and can decrease yield of crops. Although in the long term the average temperature trend is slightly constant, the yield has also declined with decreasing rainfall. Regarding the importance of wheat and lentil in arid lands of Zanjan plain, these two main rainfed crops were investigated in this study by determining the regression relationship between yield and evapotranspiration with precipitation and water stress coefficient. The results showed that the average evapotranspiration values of wheat and lentil for Zanjan plain during the study period were 398 and 262 mm and the mean temporal stress coefficients were 36 and 33%, respectively. Regression analysis showed the highest correlation between actual evapotranspiration and yield. The highest correlation was observed between temporal stress coefficient and yield of rainfed wheat. But, in lentils this trend was different and the correlation between water stress coefficient (Ks) and yield was more than the precipitation and lentil yield. For 25% decrease in precipitation, wheat yield reduced almost 18%. Generalizing this issue, the response to water stress coefficient of the product or Ky become 1.14. The regression equation also indicated that in case of being precipitation about 300 mm, wheat yield in the area will be near one tone. For 25% decrease in precipitation, wheat yield reduced about 22%. According to figures, the fitted model was significant and well-acted; and during the growth period of these crops, lack of water supply caused water stress as well as severe yield loss. The occurrence of high tension time percentage of 25% for wheat and 20% for lentil in all years of the study period confirms that the climatic potential of the region does not reach the standard requirements of plants, so it is not possible to achieve optimal performance. Hence, new methods of tillage, conservation agriculture and supplemental irrigation are recommended for achieving high yield, as well as the expected cultivation method for crops.

In the present study, firstly, the effective factors of Golestan national park forests fire have been identified. Then, by using these factors, forest fire development modeling is performed based on cellular automata. Multiple linear regression and genetic algorithm are used to determine the effective factors on Golestan national park forests fire. In order to investigate the effect of spatial resolution of the maps are used on the results of modeling, effective factors have been generated in different spatial resolution and these data are used as the input of the proposed algorithm. Also, the neighboring filters 3×3, 5×5, and 7×7 are used to investigate the effect of the neighboring filter in the forest fire development process. Cellular automata is used for modeling Golestan national park forests fire development, and the artificial bee colony is proposed to calibrate it. The results of this study show that using the proposed algorithm with 3×3 neighboring filter is more accurate than the other neighboring filters. In the best case, the Kappa index, the overall accuracy, and the relative operating characteristic are 0.924, 0.960, and 0.494, respectively that these results are for spatial resolution of 30 meters on November 17, 2010.

Soil temperature (Ts) is a key factor controlling the soil physical, chemical and biological properties and processes and consequently affects agricultural crop productions. The objective of this study was to estimate Ts from meteorological data using different machine learning methods. For this purpose, meteorological data and soil temperature at different depths (5, 10, 20, 30, 50 and 100 cm from the soil surface) for 25 years (1994-2018) were collected from 17 synoptic stations in Khuzestan province, Iran. Air temperature, wind speed, relative humidity, evaporation, precipitation, and vapor pressure were used as inputs to train the models. Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLPNN) and Extreme Learning Machine (ELM) models were used to predict soil temperature from metrological data. The results indicated that all models predicted temperature of the top layer (0-30 cm) better than the ones in sublayers. on the other hand, by increasing soil depth the accuracy of the models diminished; so that, the best and worst Ts predictions were belong to 5 cm and 100 cm depth, respectively. The results revealed that MLR, MLPNN and ELM models provided desirable performance in modeling Ts at all depths, with R2 values ranging 0.700-0.864, 0.967-0.997, and 0.967-99, RMSE values ranging 2.557–2.873, 0.034–0.072, and 0.028–0.078 °C, and MAE values ranging 1.398–1.529, 0.023–0.063, and 0.023–0.065 °C, respectively. Overall, the results showed that MLPNN and ELM models had approximately similar performance and better accuracy than MLR model. However, because of the high computational speed of the ELM model, it is recommended to use MLPNN model for estimation of soil profile Ts.