An efficient underground water prediction using optimal deep neural network

Abstract

For the humanity to the whole and all the creatures of this world, the underground water is the greatest resource to rely upon that highly forms an indispensable factor toward augmented livelihood. In spite of the lack of detailed knowledge, global warming is found to profoundly influence underground water resources through changes in underground water recharge. Prediction of the underground water under a changing climate is essential to living beings. In this article, underground water prediction using optimal deep neural networks (optimal DNN) has been attempted. Initially, the features of temperature and rainfall among the input data have been selected and after which, the chosen data have been fed to the DNN to predict the underground water. In DNN, weight parameters are optimally selected with the help of fish swarm optimization (FSO). The implementation has been done on MATLAB. The simulation results found that the proposed FSO-DNN prediction approach outperforms the existing prediction approaches by 78.9% accuracy, 83% sensitivity, 88% specificity, 95.8% positive predictive values, 52.3% negative predictive values, and 95.8% F-measure.