Predicting Groundwater Level Using the Soft Computing Tool: An Approach for Precision Enhancement

Monitoring a non-linear phenomenon—such as the groundwater levels in an aquifer—by cost-effective techniques is quite a difficult task. To overcome these limitations, soft computing tools are increasingly being used to predict groundwater levels with high accuracy. In the present study, a soft computing tool called support vector machine (SVM) was employed for predicting the groundwater levels jointly using weather parameters, at Maheshwaram watershed, Hyderabad, Andhra Pradesh, India. The accuracy of this approach was established based on statistical tools termed the regression coefficient, root mean square error, Nash-Sutcliffe coefficient, and error variation. For performance evaluation, the model outputs were compared with traditional statistical multiple regression (SMR) model outputs, and it was found that the SVR method offers better prediction than does SMR.