Prediction of groundwater level is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and reduce high costs. For this purpose, four multilayer perceptron (MLP) neural network models and four cascade forward (CF) neural network models optimized with Bayesian Regularization (BR), Levenberg-Marquardt (LM), Resilient Backpropagation (RB), and Scaled Conjugate Gradient (SCG), as well as a radial basis function (RBF) neural network model and a generalized regression (GR) neural network model were developed to predict groundwater level using 1377 data point. This data set includes 12 spatial parameters divided into two categories of sediments and bedrock, and besides, 6 time series parameters have been used. Also, to determine the best models and combine them, 165 extra validation data points have been used. After identifying the best models from the three candidate models with lower average absolute relative error (AARE) value, the committee machine intelligence system (CMIS) model has been developed. The proposed CMIS model predicts groundwater level data with high accuracy with an AARE value of less than 0.11%. Also, the proposed model was compared with ten other models through graphical and statistical error analysis. The results show that the developed CMIS model performs better than other existing models in terms of precision and validity range. The relevancy factor indicates that the electrical resistivity of sediments had the highest effect on the groundwater level. Eventually, the quality of the data used was investigated both statistically and graphically, and the results show satisfactory reliability of the data used.

The Forecasting of Groundwater Fluctuations is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and imposing high costs. In this research, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (HWES) data-driven models were used for short-term modeling of the groundwater fluctuations in a piezometer around the Gohar Zamin Iron Ore Mine. For this purpose, 250 non-seasonal groundwater fluctuations data in the period 22-Nov-2018 to 29-Jul-2019, 200 data for modeling, and 50 data for prediction were used. To take advantage of all the features of the two developed models, the predictions are combined with different methods and specific weights. The results show better accuracy for the ARIMA method between the two short-term forecasts, while the HWES method requires less time for modeling. Also, among all the predictions made, the highest accuracy for the combined least-squares method is for forecasting the groundwater fluctuations in the short-term. All the forecasts show a decrease in the groundwater fluctuations, indicating pumping wells around the Gohar Zamin Iron Ore Mine area.