Artificial Intelligence for Surface Water Quality Monitoring

Abstract

This study aims to explore the potential of machine learning algorithms, specifically artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring. The study utilizes a dataset with seven critical parameters, and the created models are evaluated based on various metrics. The goal is to categories and properly forecast the water quality index (WQI) using the suggested models. The findings show that the suggested models can accurately assess water quality and forecast WQI with high rates of success. Both the ANN and the LSTM models performed well in predicting WQI, with the ANN and LSTM model achieving an MSE and RMSE value. Temperature, pH, dissolved oxygen, conductivity, total dissolved solids (TDS), turbidity, chlorides are some of the six crucial factors used in the study's dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are some of the metrics used to develop and assess the ANN and LSTM models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which displays their correlation coefficient. The study's findings show how effective machine learning algorithms may be as a tool for monitoring surface water quality. Real-time analysis and forecasting capabilities offered by these models can aid in spotting possible problems with water quality and aid in decision-making. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With the advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. This study aims to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.