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

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 compr...

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Main Authors: Yunhwan Kim, Seoeun Kwak, Minhyeok Lee, Moon Jeong, Meeyoung Park, Yong-Gyun Park
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Water
Subjects:
Online Access:https://www.mdpi.com/2073-4441/16/1/15
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author Yunhwan Kim
Seoeun Kwak
Minhyeok Lee
Moon Jeong
Meeyoung Park
Yong-Gyun Park
author_facet Yunhwan Kim
Seoeun Kwak
Minhyeok Lee
Moon Jeong
Meeyoung Park
Yong-Gyun Park
author_sort Yunhwan Kim
collection DOAJ
description 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.
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spelling doaj.art-1e835518f27d4417b749b9abb354cdde2024-01-10T15:11:19ZengMDPI AGWater2073-44412023-12-011611510.3390/w16010015Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and PredictionYunhwan Kim0Seoeun Kwak1Minhyeok Lee2Moon Jeong3Meeyoung Park4Yong-Gyun Park5Department of Environmental and Energy Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of KoreaDepartment of Computer Engineering, Kyungnam University, 7 Gyeongnamdaehak-ro, Masanhappo-gu, Changwon-si 51767, Republic of KoreaDepartment of Environmental and Energy Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of KoreaDepartment of Environmental and Energy Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of KoreaDepartment of Computer Engineering, Kyungnam University, 7 Gyeongnamdaehak-ro, Masanhappo-gu, Changwon-si 51767, Republic of KoreaDepartment of Environmental and Energy Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of KoreaThe 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.https://www.mdpi.com/2073-4441/16/1/15optimal intake layerwater treatmentdrinking water sourcewater qualitydeep learning model
spellingShingle Yunhwan Kim
Seoeun Kwak
Minhyeok Lee
Moon Jeong
Meeyoung Park
Yong-Gyun Park
Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction
Water
optimal intake layer
water treatment
drinking water source
water quality
deep learning model
title Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction
title_full Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction
title_fullStr Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction
title_full_unstemmed Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction
title_short Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction
title_sort determination of optimal water intake layer using deep learning based water quality monitoring and prediction
topic optimal intake layer
water treatment
drinking water source
water quality
deep learning model
url https://www.mdpi.com/2073-4441/16/1/15
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AT minhyeoklee determinationofoptimalwaterintakelayerusingdeeplearningbasedwaterqualitymonitoringandprediction
AT moonjeong determinationofoptimalwaterintakelayerusingdeeplearningbasedwaterqualitymonitoringandprediction
AT meeyoungpark determinationofoptimalwaterintakelayerusingdeeplearningbasedwaterqualitymonitoringandprediction
AT yonggyunpark determinationofoptimalwaterintakelayerusingdeeplearningbasedwaterqualitymonitoringandprediction