Predicting water quality in purification plants: a simulation system

Abstract The quality of drinking water is a pivotal global concern, with water purification plants striving to ensure the safety and potability of the water supply. Traditional methods of water quality assessment are being augmented by advanced predictive analytics, which offer the potential for more efficient and accurate monitoring. One of the main challenges is accurately predicting water potability using diverse datasets with varying quality metrics. The complexity of water quality-related data and the need for timely decision-making call for robust predictive models that can handle high-dimensional datasets. This study introduces a simulation system that employs various machine learning classifiers, including traditional algorithms such as Logistic Regression, SVM, Decision Trees, and Random Forests, as well as advanced deep learning techniques such as CNN, LSTM, BI-LSTM, CNN-BI-LSTM, GRU, and BI-GRU. Performance evaluations are conducted using ROC curves and AUC metrics, comparing the efficacy of each model in predicting water potability. The deep learning classifiers, particularly CNN, demonstrated superior performance with a perfect AUC score of 1.00. However, this suggests potential overfitting, prompting further validation. BI-LSTM and BI-GRU also yielded high AUCs, indicating their robustness in capturing sequential patterns in the data. The implications of these findings are substantial for water purification plants, suggesting that implementing deep learning models could significantly enhance the prediction of water quality and potability. By transitioning to these advanced predictive models, plants can potentially achieve more accurate, real-time water quality monitoring, leading to improved public health outcomes.