Modeling of groundwater level using artificial neural network algorithm and Wa-Svr model - Gaur S.; Johannet A.; Graillot D.; Omar P.J. (My University, 2025)
This chapter describes Artificial Neural Network optimization technique and Wavelet Support Vector Machine (WA-SVR) model for predicting the groundwater level in the given terrain. Ground water forecasting is significantly needed for groundwater management. The most important purpose of applying the neural network (artificial) was to extract the probability of various algorithms. Now-a-days, Artificial Neural Networks (ANN) are the most widely accepted modeling technique which can approximate the relationship (nonlinear) between input datasets and output results without considering physical processes and the corresponding equations of the system. Due to this capability, an ANN model is much faster than a physically based model which it approximates. Prediction of accurate groundwater head helps in the practical and best possible usage of the water resources. The performance of the models was evaluated by using two performance measures, the correlation coefficient (R) and Nash-Sutcliffe Efficiency Index (Ef). Model accuracy has been improved using different network architectures and training algorithms. After improving the model accuracy, it has been noticed that the best results can be accomplished with a typical feedforward neural network (FFNN) trained with the Levenberg-Marquardt (LM) algorithm obtained for simulation of ground water levels. The results revealed that ANN model technique was appropriate for predicting the groundwater heads. The study conclusively confirms the ability of ANNs to give the precise estimation of the head value with fair accuracy. From the results, it has been concluded that the ANNs have simulated and predicted the water heads in the river under acceptable residuals, whereas WA-SVR model's results are more accurate. Study concludes that wavelet decomposition-based SVR is found superior in comparison of the ANN model. © The Editor(s) (if applicable) and The Author(s), 2021. All rights reserved.