Application of Wann model for Groundwater level forecasting in Ur river watershed in Tikamgarh district, India

Abstract

The use of Aquifers as a source of water supply is increasing on a global scale, leading to over-exploitation of available groundwater blocks. Thus, there is an increasing demand for checking the groundwater levels for better and sustainable management of groundwater resources. To acquire knowledge about the factors affecting the entire groundwater system, one should know the important variables and how they vary over time. It is well known that the groundwater head is considered to be one of the most essential hydrological variables and hence, it is monitored and predicted frequently at different locations and at frequent time intervals. Particularly, the groundwater prediction in hard rock areas is a complex task with the use of physically-based models as compared to the data-driven models. Therefore, in this study, an attempt has been made to verify the adequacy as well as the efficacy of the Artificial Neural Network model (ANN) and Wavelet-ANN conjunction (WANN) models in the prediction of groundwater levels in the Ur River watershed in Tikamgarh district of Madhya Pradesh, India. Although the Ur river basin having mainly granite type of aquifer, the obtained results reveal that the WANN and ANN models can be used to predict the groundwater levels in this watershed. The application of the ANN model in the groundwater prediction gives a higher estimate of the RMSE values during calibration and validation as compared to those obtained with the application of the WANN model for each one of the observation wells. Further, the WANN model is capable to provide groundwater level prediction with higher efficiency as reflected by higher R2 values during calibration and validation as compared to the ANN model which indicates a substantial improvement in the model performance. Therefore, it can be concluded that the WANN model provides a significantly accurate prediction of groundwater levels as compared to the results of the ANN model. Besides, the comparison of the scatter plots of time series during calibration and validation indicates that the values of water level depth estimated by the WANN model are more precise than those estimated by the ANN. Thus, this paper reveals the significant features of ANN models for forecasting groundwater levels in hard rock aquifer and their performance enhancement with wavelet theory.