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Soil erosion and sediment transport measurement is a time-consuming and difficult step yet important part of hydrological studies. Hence, use of models has become commonplace in estimating soil erosion and sediment transport. In this study, we used an artificial neural network (ANN) to simulate soil erosion rates. A geographic information system (GIS) was used as a pre-processor and post-processor tool to present the spatial variation of the soil erosion rate. The ANN was trained, optimized and verified using data from the Kasilian watershed located in the northern part of Iran. Field plots were used to estimate soil erosion values on the hillslopes. A Multi Layer Perceptron (MLP) network was adopted, where the soil erosion rate was the output variable and the rainfall intensity and amount, air and soil temperature, soil moisture, vegetation cover and slope were the inputs. After the training process, the network was tested. According to the test results, the ANN can estimate soil erosion with an acceptable level (coefficient of determination = 0.94, mean squared error = 0.04). The verified network and its inputs were used to estimate soil erosion rates on the hillslopes. Finally, a soil erosion rate map was generated based on the results of the verified network and GIS capabilities. The results confirm the high potential when coupling an ANN and a GIS in soil erosion estimation and mapping on the hillslopes. [ABSTRACT FROM AUTHOR]

Dendrohydrogeology can be used to simulate historical groundwater depth, water table drawdown, groundwater recharge and piezometric lines. We simulated paleohydrogeologic conditions via tree-rings and vessel chronologies using an artificial neural network (ANN) in the alluvial aquifer of the Caspian southern coast of Iran during the past century. Tree-ring width, vessel features, secondary piezometric well data, and precipitation from different sites within the study area were evaluated. After cross-dating, standardization and time series analysis, the relationships between tree-rings and vessel chronologies with groundwater depth were defined and simulated. Additionally, paleohydrogeologic records during the past century were simulated. The results generally demonstrate that tree-ring width is a better index than vessel features. However, we obtained the most exact groundwater depth modeling results by using the combination of tree-rings and earlywood vessel diameter from periods of low precipitation and groundwater fluctuations and significant temperature fluctuations. We also found that dendrohydrogeology has more applicability in groundwater modeling in areas where groundwater depth fluctuates 10–20 m below ground surface (based on root depth and water access). Moreover, using the simulated groundwater depths, piezometric lines in 1927 and 2000 (the years with maximum natural recharge and maximum drawdown respectively) were extracted using an interpolation technique and Geographic Information System (GIS). Finally, we suggest applying dendrohydrogeology for paleohydrogeologic modeling in alluvial aquifers. [ABSTRACT FROM AUTHOR]

Increased nitrate concentration is one of the main groundwater quality problems today that needs to be measured and monitored. Water quality testing and monitoring are time consuming and costly. Therefore, new modeling methods such as machine learning algorithms can be used as an efficient solution for predicting nitrate concentration. In this study, three machine learning methods including deep neural network (DNN), extreme gradient boosting (EGB), and multiple linear regression (MLR) were used to predict nitrate contamination in groundwater in the north of Iran (Mazandaran plain) and finally the best method was selected for mapping. The mean nitrate concentration in 250 piezometric wells was considered as output variable and the factors affecting groundwater quality (groundwater depth, transmissivity of aquifers, precipitation, evaporation, distance from water resources and Caspian Sea, distance from industries and residential centers, population density, topography, and exploitation from groundwater) as input variables in an alluvial aquifer. The same training and testing data were used in the modeling process of the three machine learning methods. The results of the training and testing stages showed that the EGB method has the highest performance in predicting nitrate concentration due to the lowest error values and highest correlation between the measured and predicted values of nitrate concentration (training R-sqr = 0.98, Nash–Sutcliffe efficiency (NSE) = 0.98, and test R-sqr = 0.86, NSE = 0.84). Further, the results indicate that the factors distance from industries, population density, groundwater depth, and evaporation rates are the most important factors affecting nitrate concentration in groundwater. Finally, the tested EGB model and a geographic information system (GIS) tool were used to prepare a map of groundwater nitrate pollution in the study area. Evaluating the performance of the resulting map by comparing the predicted and measured values indicated a good accuracy (R-sqr = 0.8). • Machine learning was used for modeling nitrate concentration in groundwater. • The extreme gradient boosting model yielded more favorable results than the deep neural network and multiple linear regression methods. • The verification of the results showed the performance of the adopted methodology in nitrate concentration mapping. [ABSTRACT FROM AUTHOR]