جستجوی مقالات مرتبط با کلیدواژه « رگرسیون خطی چندگانه » در نشریات گروه « آب و خاک »

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.

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.

Evapotranspiration plays an imperative role in management of regional water resources, climate change and agricultural production. In this study efficiency of some data-driven techniques, including support vector machine (SVM), artificial neural networks (ANN) and its hybrid with wavelet transform (WANN), multi linear regression (MLR) and decision tree (DT) for predicting Evapotranspiration rates at Scottsbluff Station in Nebraska have been monitored. For this purpose, 5 meteorological parameters utilized as inputs for the models. Daily meteorological information, data used in this study, were between 2005 - 2013 years to train and test the models. In order to implement each of the models 8 scenarios were considered according to combination of input parameters. For evaluate performance of the studied techniques, three different statistical indices were used which included root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe coefficient (NSE). In addition, Taylor charts were used to test similarity between observation and prediction data. The results showed that at the Scottsbluff station, WANN8 (is the eighth scenario for the WANN model) according to the root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe equal to 0.097, 0.999 and 0.999 performed better than ANN, SVM, MLR and DT. The SVM and ANN models also showed excellent accuracy, and the DT and MLR models performed worse than the other models despite their acceptable accuracy. As a conclusion, the results of the present study were proved that WANN provides reasonable procedures for modeling Scottsbluff at the Scottsbluff station.

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.

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.

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.

Statistical analysis and forecast discharge data play an important role in management and development of water systems. The most fundamental issues of statistical analysis and forecast discharge in Iran are lack of data in long term period and lack of stream flow data in gauging stations. Considering the issues mentioned in this study, we tried to estimate the daily data flow (runoff) of Santeh gauging station in Kordestan province using the nearby hydrometric and meteorological stations data. This estimation occurred based on the sixteen different input combinations, including data of daily flow of hydrometric stations Safakhaneh and Polanian and daily runoff in Santeh precipitation gauging station. In this research, the daily flow estimation of the Santeh station in each of the months of the year was evaluated for sixteen different combinations and artificial neural network models and multiple linear regressions. The performance of each model was evaluated with the indicators RMSE, CC, NS and t-student statistic. The results showed good performance of both models but the performance of the artificial neural network model was better than the regression model in estimation of the daily runoff in the most months of the year. Mean error of artificial neural network and multiple linear regression models was respectively estimated as 6.31 and 8.07 m3/s in the months of the year. It should be noted that the artificial neural network, for each sixteen combination used, had better result than the regression model.

Climate variability is the most effective parameter on crop productivity especially rainfed crops.in this study, the anomaly index was calculated for meteorological elements such as maximum temperature(T max), minimum temperature(T min), precipitation(P) and crop yield(Y) for Razavi Khorasan province in Iran, which is located in arid and semi arid region of the world. Precipitation Anomaly Percentage (PAP) is a traditional drought monitoring index, it shows the precipitation as a percentage of the long-term average or normal Thepurpose of this study is finding significant relation between various indexes such as yearly precipitation anomaly (PA) with yearly maximum temperature anomaly(T max A) and yearly minimum temperature anomaly(T min A) for the period (1989-2005).At first, moving average of 5, years old,were examined for 10 stations. In addition, yearly precipitation anomaly, probability and return period of it were calculated using sigmoidfunction. Pearson correlation was examined to find relationship between change percent of yearly precipitation anomaly(PAP), yearly minimum(TminAP), maximum temperature anomaly(TmaxAP) and yearly rainfed crop yield anomaly of wheat (YAP). Multiple linear regression was calculate between YAP (as dependent parameter) and climate variability(indpendent parameter). Sarakhs, Ghuchan and Mashhad had significant correlation (P <0.05) 0.63,0.55 and 0.54 respectively. In other station, no relation found between PAP and YAP. A significant relation between TmaxAP and YAP was obtained in Mashhad (P < 0.05, r= -0.48) and TminAP and YAP was found only in Ghuchan(r=0.56).

This study was done in order to submit a model for salinity map made with ETM+ satellite data and salinity values in a district in North of Agh Ghala in Golestan province. The Geometric and atmospheric correction of Satellite images were carried out Processings such as; multispectral bands fusion with panchromatic band, tasseled cap transformation, principal component analysis, and ratioing for composite bands creation were done. The regular sampling grid with 2×10 km dimension and different intervals were designed and performed on the study area. Soil samples (169 samples) were taken from 0-5 cm depth with intervals of 50, 100, 250, 1000 m and their electrical conductivity (EC) were measured. The location of the samples was determined by GPS and samples location map was made with GIS software. Spectral values for each sample were extracted by different bands. Correlation between spectral values i.e. (main bands, tasseled cap bands, principal component analysis and composite bands) with electrical conductivity values were investigated for 80% of the samples. By determining correlated bands and selecting desirable bands for contributing in model, primary results showed that correlation between electrical conductivity values with B4 bands, brightness, greenness and fuse B4 were significant in 1% level. After selecting the best model, cross validation was calculated with 20% samples for determinating ME and RMSE parameters. Cross validation results showed the suitable estimation of model with ME and RMSE values was equal to -0.18 and 55.6 dS/m, respectively. Finally, salinity map was prepared using submitted regression model. In this research, results confirmed that salinity changes can be evaluated by digital data of ETM+ and it's composites in district of north Agh Ghala with 2000 ha area.