Analyzing groundwater level with hybrid ANN and ANFIS using metaheuristic optimization

The analysis of groundwater resources is extremely important to the cultivation of crops, our daily lives, and sustainable growth. Thus, a precise and credible estimation of groundwater levels is crucial and helps to prevent resource depletion. Research in the past has shown that machine-learning approaches such as Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are effective at mimicking complicated nonlinear problems and also integrating multiple techniques to develop an enhanced tool that improves the accuracy of the prediction model. This study presented prediction models that constitute hybrid techniques integrating ANN, ANFIS, and an Improved Reptile Search Algorithm (IRSA). In this case, IRSA is applied to identify the parameters of the ANN and ANFIS in order to improve the overall effectiveness of the forecasting models. After this, the developed hybrid approaches for groundwater level modelling were evaluated using four seasons (January to March, April to June, July to September, and October to December) of groundwater level data for India collected from the India Water Resources Information System. Also, the comparative study has done between ANN-IRSA, ANFIS-IRSA, and traditional ANN as well as ANFIS, which were evaluated on the same datasets. The ANFIS-IRSA model achieved optimal RMSE (0.4074, 0.3927, 4.5591, 1.8408), MAE (0.2329, 0.2516, 1.3644, 0.8612), MAPE (0.0201, 0.022, 0.04, 0.0664), R2 (0.9907, 0.9861, 0.9747, 0.9809), WI (0.9975, 0.9963, 0.9926, 0.9948), and PBIAS (0.2862, 0.0833, 2.7417, 1.7682) for four distinct seasons, which is exceptional in comparison with other models. Based on the results of simulations and comparisons, ANFIS-IRSA outscored other models on the same datasets and proved to be a robust method to predict time-series data.