Evaluation of machine learning algorithms for groundwater quality modeling.

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings.

Evaluating the effects of vegetation and land management on runoff control using field plots and machine learning models.

Excess surface water after heavy rainfalls leads to soil erosion and flash floods, resulting in human and financial losses. Reducing runoff is an essential management tool to protect water and soil resources. This study aimed to evaluate the effects of vegetation and land management methods on runoff control and to provide a model to predict runoff values. Filed plot data and three machine learning (ML) methods, including artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIS), and extreme gradient boosting (EGB), were used in a test site in the north of Iran. In this regard, plots with various vegetation and land management treatments including bare soil treatment, rangeland cover treatment, forest litter treatment, rangeland litter treatment, tillage treatment in the direction of slope, tillage treatment perpendicular to the slope, and repetition of treatments under forest canopy were constructed on a hillslope. After each rainfall event, the amount of rainfall and corresponding runoff generated in each plot was recorded. Three ML models (ANN, CANFIS, and EGB) were used to establish relationships between amounts of recorded runoff and its controlling factors (rainfall, antecedent soil moisture (A.M.C), shrub canopy percentage and height, tree canopy percentage and height, soil texture (clay, silt, and sand percent), slope degree, leaf litter percentage of soil, and tillage interval). These data were normalized, randomized, and divided into training and testing subsets. Results showed that the ANN performed better than the other two models in predicting runoff in training (R2 = 0.98; MSE = 0.004) and the test stages (R2 = 0.90; MSE = 0.95). Statistical analysis and sensitivity analysis of inputs factors showed that rainfall, rangeland cover, and A.M.C are the three most important factors controlling runoff generation. The adopted method can be used to predict the effect of different vegetation and land management scenarios on runoff generation in the study area and the areas with similar settings elsewhere.

Prediction of groundwater drawdown using artificial neural networks.

Groundwater drawdown is typically measured using pumping tests and field experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on artificial neural network (ANN)s and field measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the influence radius of each well (1000 m) were identified and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown ​​(Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was fitted to available input data to map the annual drawdown ​​across the entire aquifer. The accuracy assessment of the final map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere.