Prediction of Groundwater Level Using Hybrid SVM-FFA Approach

Groundwater which is the most valuable resource available on the Earth`s surface and is used for drinking, irrigation, livestock, etc is depleting day by day. Groundwater level prediction faces complex challenges to sustainably manage this vital resource. Predicting groundwater level is crucial for water resource management. Here this study explores the use of some hybrid machine learning models such as SVM-FFA, SVM-PSO and compared with the stand alone SVM approach. Then introducing an innovative approach for predicting groundwater level with improved accuracy and to enhance the performance of the model and face the challenges developed during the process. This work investigates hybrid machine learning techniques to improve the accuracy of groundwater levels predictions, which are constrained in conventional hydrological models. The study uses long time series of monthly data from 2008-2024 taking precipitation, evaporation, temperature, and relative humidity as input features from Balipatana block of Khordha district of Odisha, India. In this analysis, performance metrices like Root Mean Square Error (RMSE), Coefficient of determination (R²), Mean Absolute Error (MAE) and Willmott Index (WI) were employed. It is found that the value of RMSE, R², MAE, WI are 8.5832, 95.6756, 10.9438, 94.2981; 13.2287, 93.0073, 15.9084, 91.6327; 21.9627, 88.2165, 24.1689, 86.8491 in case of SVM-FFA, SVM-PSO and SVM respectively. The results demonstrates that the SVM-FFA models performs much better than the SVM-PSO and standalone methos in improving their accuracy and their robustness. With high prediction exactness and strategic versatility, the proposed model proved a powerful selection for forecasting groundwater levels.