Machine Learning-based Water Quality Forecasting for Shenzhen Bay

Based on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality paramet...

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Main Authors: XIONG Jianzhi, XIONG Rui, LU Haiyan, ZHENG Yi
Format: Article
Language:zho
Published: Editorial Office of Pearl River 2024-07-01
Series:Renmin Zhujiang
Subjects:
Online Access:http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.002
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author XIONG Jianzhi
XIONG Rui
LU Haiyan
ZHENG Yi
author_facet XIONG Jianzhi
XIONG Rui
LU Haiyan
ZHENG Yi
author_sort XIONG Jianzhi
collection DOAJ
description Based on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality parameters such as dissolved oxygen (DO), chlorophyll-a (Chl.a), total nitrogen (TN), and total phosphorus (TP). The research findings indicate that utilizing high-frequency in-situ water quality monitoring data enables accurate prediction of water quality in Shenzhen Bay within 24 hours. Specifically, ANN is found to be the most suitable for forecasting DO, Chl.a, and TN, with nash-sutcliffe efficiency (NSE) values greater than 0.60 for the 24-hour forecast period. Meanwhile, the RF model is found to be the most suitable for TP forecasting, with NSE values greater than 0.76 within 24 hours. The findings of this study have important implications for the precise prevention and control of water pollution in the Guangdong-Hong Kong-Macao Greater Bay Area.
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institution Kabale University
issn 1001-9235
language zho
publishDate 2024-07-01
publisher Editorial Office of Pearl River
record_format Article
series Renmin Zhujiang
spelling doaj-art-f4c639d773104a3291a4ea61301052f52025-01-15T03:01:15ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-07-0145101866690810Machine Learning-based Water Quality Forecasting for Shenzhen BayXIONG JianzhiXIONG RuiLU HaiyanZHENG YiBased on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality parameters such as dissolved oxygen (DO), chlorophyll-a (Chl.a), total nitrogen (TN), and total phosphorus (TP). The research findings indicate that utilizing high-frequency in-situ water quality monitoring data enables accurate prediction of water quality in Shenzhen Bay within 24 hours. Specifically, ANN is found to be the most suitable for forecasting DO, Chl.a, and TN, with nash-sutcliffe efficiency (NSE) values greater than 0.60 for the 24-hour forecast period. Meanwhile, the RF model is found to be the most suitable for TP forecasting, with NSE values greater than 0.76 within 24 hours. The findings of this study have important implications for the precise prevention and control of water pollution in the Guangdong-Hong Kong-Macao Greater Bay Area.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.002water quality forecastingmachine learningShenzhen Bay
spellingShingle XIONG Jianzhi
XIONG Rui
LU Haiyan
ZHENG Yi
Machine Learning-based Water Quality Forecasting for Shenzhen Bay
Renmin Zhujiang
water quality forecasting
machine learning
Shenzhen Bay
title Machine Learning-based Water Quality Forecasting for Shenzhen Bay
title_full Machine Learning-based Water Quality Forecasting for Shenzhen Bay
title_fullStr Machine Learning-based Water Quality Forecasting for Shenzhen Bay
title_full_unstemmed Machine Learning-based Water Quality Forecasting for Shenzhen Bay
title_short Machine Learning-based Water Quality Forecasting for Shenzhen Bay
title_sort machine learning based water quality forecasting for shenzhen bay
topic water quality forecasting
machine learning
Shenzhen Bay
url http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.002
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AT xiongrui machinelearningbasedwaterqualityforecastingforshenzhenbay
AT luhaiyan machinelearningbasedwaterqualityforecastingforshenzhenbay
AT zhengyi machinelearningbasedwaterqualityforecastingforshenzhenbay