Application of Bi-LSTM method for groundwater quality assessment through water quality indices

Water is a crucial resource in all economic activity, from farming to manufacturing. Water supplies are under much stress due to the ever-growing strain of the world population. Hence, effective water management is crucial to civilized society for raising living standards. To address the problems with drinking water quality, ground-water quality has to be frequently checked. Utilizing the water quality index, an effort has been made to comprehend groundwater quality (WQI). It is a technique for assessing water quality and a beneficial tool for determining how groundwater quality has changed over time and in different locations. Managing ecological systems and water resources can benefit greatly from machine learning approaches computation and consider-able forecasting errors. Hence the deep neural network based long short-term memory network (LSTM), has been used for high performance. This study presents a deep learning-based Bi-LSTM model to predict the variables affecting groundwater quality. The suggested model's effectiveness was compared to several existing methods, including LSTM, RNN, and GRU. According to a comparative analysis, the suggested model has 0.98 % of ac-curacy and precision which exceeds all other approaches in terms of the best prediction performance, and it may serve as a decision-making basis for the comprehensive management of water quality.

Automated summary

Water is a crucial resource in economic activities and effective management is essential. The quality of groundwater can be assessed using the water quality index, and machine learning models such as the LSTM can greatly improve prediction performance. This study presents a deep learning-based Bi-LSTM model for predicting groundwater quality variables.