A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River

Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has bec...

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Autores principales:	Hanjie Yang, Zhaoting Chen, Yingxin Ye, Gang Chen, Fantang Zeng, Changjin Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fuzzy logic early warning algal blooms eutrophication Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/6e857046e46b4d25abb7516e5289388f
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spelling	oai:doaj.org-article:6e857046e46b4d25abb7516e5289388f2021-11-11T19:57:52ZA Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River10.3390/w132131182073-4441https://doaj.org/article/6e857046e46b4d25abb7516e5289388f2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3118https://doaj.org/toc/2073-4441Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has become a robust tool to establish early warning systems. In this study, a framework for an early warning system was constructed, aiming to accurately predict algae blooms in a river containing several water conservation areas and in which the operation of two tidal sluices has altered the tidal currents. Statistical analysis of sampled data was first conducted and suggested the utilization of dissolved oxygen, velocity, ammonia nitrogen, total phosphorus, and water temperature as inputs into the fuzzy logic model. The fuzzy logic model, which was driven by biochemical data sampled by two auto-monitoring sites and numerically simulated velocity, successfully reproduced algae bloom events over the past several years (i.e., 2011, 2012, 2013, 2017, and 2019). Considering the demands of management, several key parameters, such as onset threshold and prolongation time and subsequent threshold, were additionally applied in the warning system, which achieved a critical success index and positive hit rate values of 0.5 and 0.9, respectively. The differences in the early warning index between the two auto-monitoring sites were further illustrated in terms of tidal influence, sluice operation, and the influence of the contaminated water mass that returned from downstream during flood tides. It is highlighted that for typical tidal rivers in urban areas of South China with sufficient nutrient supply and warm temperature, dissolved oxygen and velocity are key factors for driving early warning systems. The study also suggests that some additional common pollutants should be sampled and utilized for further analysis of water mass extents and data quality control of auto-monitoring sampling.Hanjie YangZhaoting ChenYingxin YeGang ChenFantang ZengChangjin ZhaoMDPI AGarticlefuzzy logicearly warningalgal bloomseutrophicationHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3118, p 3118 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuzzy logic early warning algal blooms eutrophication Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	fuzzy logic early warning algal blooms eutrophication Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Hanjie Yang Zhaoting Chen Yingxin Ye Gang Chen Fantang Zeng Changjin Zhao A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River
description	Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has become a robust tool to establish early warning systems. In this study, a framework for an early warning system was constructed, aiming to accurately predict algae blooms in a river containing several water conservation areas and in which the operation of two tidal sluices has altered the tidal currents. Statistical analysis of sampled data was first conducted and suggested the utilization of dissolved oxygen, velocity, ammonia nitrogen, total phosphorus, and water temperature as inputs into the fuzzy logic model. The fuzzy logic model, which was driven by biochemical data sampled by two auto-monitoring sites and numerically simulated velocity, successfully reproduced algae bloom events over the past several years (i.e., 2011, 2012, 2013, 2017, and 2019). Considering the demands of management, several key parameters, such as onset threshold and prolongation time and subsequent threshold, were additionally applied in the warning system, which achieved a critical success index and positive hit rate values of 0.5 and 0.9, respectively. The differences in the early warning index between the two auto-monitoring sites were further illustrated in terms of tidal influence, sluice operation, and the influence of the contaminated water mass that returned from downstream during flood tides. It is highlighted that for typical tidal rivers in urban areas of South China with sufficient nutrient supply and warm temperature, dissolved oxygen and velocity are key factors for driving early warning systems. The study also suggests that some additional common pollutants should be sampled and utilized for further analysis of water mass extents and data quality control of auto-monitoring sampling.
format	article
author	Hanjie Yang Zhaoting Chen Yingxin Ye Gang Chen Fantang Zeng Changjin Zhao
author_facet	Hanjie Yang Zhaoting Chen Yingxin Ye Gang Chen Fantang Zeng Changjin Zhao
author_sort	Hanjie Yang
title	A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River
title_short	A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River
title_full	A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River
title_fullStr	A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River
title_full_unstemmed	A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River
title_sort	fuzzy logic model for early warning of algal blooms in a tidal-influenced river
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6e857046e46b4d25abb7516e5289388f
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A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River

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