Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction

Abstract

The effective management of drinking water sources is essential not only for maintaining their water quality but also for the efficient operation of drinking water treatment plants. A decline in water quality in water reservoirs can result in increased operational costs for water treatment and compromise the reliability and safety of treated water. In this study, a deep learning model, the long short-term memory (LSTM) algorithm, was employed to predict water quality and identify an optimal water intake layer across various seasons and years for Juam Lake, Korea. A comprehensive investigation was conducted to prioritize various water quality parameters and determine suitable intake layers. Based on these priorities, effective methods for optimizing an intake layer were developed to enable more reliable water intake operations. Water quality data from January 2013 to June 2023 were analyzed for the study. This dataset was used for rigorous statistical and correlational analyses to better understand the dynamics affecting water quality parameters. The findings aim to enhance the operational efficiency of water intake and treatment facilities.