Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) model for Forecasting Groundwater Level in the Pravara River Basin, India

The precise prediction of groundwater level is essential for water reserve management. In this study, the two intelligence models, viz, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), for predicting groundwater levels in Pravara River seven groundwater stations such as Sangamner, Deolali, Shrirampur, Rahata, Sonai, Taklibhan, and Loni are examined. To forecast groundwater level, 19 years' (2000–2018) data sets were used as target data including rainfall and temperature as input  data. The results confirmed that ANN and ANFIS models can precisely predict groundwater level. The ANFIS model, which had R2 = 81.70, RMSE = 1.389, NS = 0.817, and NMSE = 0.193, outperformed the ANN model, which had an R2 = 76.32, RMSE = 0.539, NS = 0.584, and NMSE = 0.159. By comparing the observed and predicted groundwater levels at the Sangamner, Deolali, Shrirampur, Rahata, Sonai, Takali, and Loni groundwater monitoring stations using the best ANN and ANFIS model, it can be seen that the two values are nearly close to each other. The unknown value at the Sonai, Taklibhan, and Loni station from 2000 to 2014 is predicted using the Inverse Distance Weighting (IDW) interpolation method. The inference drawn from this study would be beneficial to groundwater management.