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...
Main Authors: | , , , , , |
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Format: | Article |
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MDPI AG
2023-12-01
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Series: | Water |
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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. |
first_indexed | 2024-03-08T14:55:01Z |
format | Article |
id | doaj.art-1e835518f27d4417b749b9abb354cdde |
institution | Directory Open Access Journal |
issn | 2073-4441 |
language | English |
last_indexed | 2024-03-08T14:55:01Z |
publishDate | 2023-12-01 |
publisher | MDPI AG |
record_format | Article |
series | Water |
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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