Predicting Groundwater Quality with Advanced Deep Learning Models: Improving Environmental Management by Addressing Pollution Issues and Obtaining More Accurate Forecasts

To ensure clean water for consumption in the future, it is imperative to understand the pollutants and the quality of the existing groundwater. Controlling water pollution and enhancing water management both depend on the ability to predict the water’s purity with high accuracy. Previous studies have shown that when it comes to deep learning models, extreme gradient boosting (XGBoost) performs better in terms of accuracy and precision than random forest (RF). This study explores the field of water quality assessment by creating a novel method that combines state-of-the-art deep learning algorithms such as artificial neural networks (ANN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), recurrent neural networks (RNN), and backpropagation neural networks (BPNN). Utilising deep learning techniques to enhance the index’s prediction capacity and accuracy, as well as computing WQI utilising comprehensive water quality metrics, are our main research objectives. Through the use of deep learning models in conjunction with WQI, we hope to uncover patterns and connections in information on water quality that are not visible through conventional means. Plans for groundwater development will benefit from the study’s findings, which will also enhance DL applications for better groundwater quality.