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Understanding annual extraction volumes and fluctuations in groundwater depth is essential in water resource management. This study applied various machine learning methods to predict the spatial variability of groundwater depletion (GWD) in an alluvial aquifer located along the southern coasts of the Caspian Sea (Mazandaran Plain). Initially, mean GWD values were measured from 250 piezometric wells across the plain. Subsequently, the factors controlling GWD were identified and provided. These factors encompass natural elements, including precipitation, evaporation, topography, groundwater depth, aquifer transmissivity, and proximity to water bodies. They also include anthropogenic factors such as extraction rates and proximity to industrial centres. Three machine learning models – Random Forest (RF), Deep Learning (DL), and Extreme Gradient Boosting (EGB) – were employed to model GWD using consistent training and test data. In this context, GWD was used as the output, and its controlling factors were incorporated as input variables. Correlation coefficients between GWD and its controlling factors revealed that the transmissivity of aquifer formations (R = 0.81), groundwater withdrawal within well radii (R = 0.69), and groundwater depth (R = 0.6) were the most significant factors. All three models demonstrated high efficacy during the training and testing stages. However, during testing, EGB offered the highest performance in modelling GWD due to extraction (R2 = 0.8, NSE = 0.82). The verified model was then used to predict annual drawdown or extraction across the plain. The predicted results were visually represented as an annual GWD or extraction map within a GIS framework. A comparison between observed GWD values in piezometric wells and the predicted values confirmed the robust performance of the proposed methodology (R2 = 0.8). This methodology can be employed effectively to predict spatial changes in GWD or annual extraction in other plains. [ABSTRACT FROM AUTHOR]

Precipitation forecasting plays a key role in natural resource management, agriculture management, and water requirement provision. Hence, dendroclimatology methods and artificial neural network (ANN) are used to estimate precipitation values in the past times. Moreover, a geographic information system (GIS) can be applied as a tool to demonstrate the spatial variation of precipitation. In this study, earlywood vessel features and tree-rings of Siberian elm species were used to simulate precipitation. The vessel features, the tree-ring extent, and the secondary data of climatologic station from the different sites were studied. At first, cross-dating and standardization of the tree-rings and vessels were performed. Then, time series analysis was done. In the next step, the relations between vessel chronologies and tree-rings with the precipitation were defined. The input parameters were selected tree-ring width and vessel features for the modeling, whereas precipitation of the growing season was selected as the output. The model or network was trained and verified by taking a case study of Fomanat plain (northern part of Iran). The results showed that the combinatory usage of vessel features and tree-rings extent in precipitation simulating can increase simulation capability. Further, different isohyets were generated using the simulated precipitation values, an interpolation technique in a GIS system. The results showed that the highest precipitation and the lowest precipitation have occurred in 1926 and 1986 during the last century. [ABSTRACT FROM AUTHOR]

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]

Summary Groundwater is the most important water resource in semi-arid and arid regions such as Iran. It is necessary to study groundwater level fluctuations to manage disasters (such as droughts) and water resources. Dendrochronology, which uses tree-rings to reconstruct past events such as hydrologic and climatologic events, can be used to evaluate groundwater level fluctuations. In this study, groundwater level fluctuations are simulated using dendrochronology (tree-rings) and an artificial neural network (ANN) for the period from 1912 to 2013. The present study was undertaken using the Quercus Castaneifolia species, which is present in an alluvial aquifer of the Caspian southern coasts, Iran. A multilayer percepetron (MLP) network was adopted for the ANN. Tree-ring diameter and precipitation were the input parameters for the study, and groundwater levels were the outputs. After the training process, the model was validated. The validated network and tree-rings were used to simulate groundwater level fluctuations during the past century. The results showed that an integration of dendrochronology and an ANN renders a high degree of accuracy and efficiency in the simulation of groundwater levels. The simulated groundwater levels by dendrochronology can be used for drought evaluation, drought period prediction and water resources management. [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]