Integrated ensemble learning approach for multi-depth water quality estimation in reservoir environments

Zare, MS; Nikoo, MR; Al-Rawas, G; Nazari, R; Al-Wardy, M; Etri, T; Gandomi, AH

Integrated ensemble learning approach for multi-depth water quality estimation in reservoir environments

Zare, MS Nikoo, MR Al-Rawas, G Nazari, R Al-Wardy, M Etri, T Gandomi, AH

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Water Process Engineering, 2024, 66, pp. 105840
Issue Date:: 2024-09-01

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Field	Value	Language
dc.contributor.author	Zare, MS
dc.contributor.author	Nikoo, MR
dc.contributor.author	Al-Rawas, G
dc.contributor.author	Nazari, R
dc.contributor.author	Al-Wardy, M
dc.contributor.author	Etri, T
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-10-10T01:38:01Z
dc.date.available	2024-10-10T01:38:01Z
dc.date.issued	2024-09-01
dc.identifier.citation	Journal of Water Process Engineering, 2024, 66, pp. 105840
dc.identifier.issn	2214-7144
dc.identifier.issn	2214-7144
dc.identifier.uri	http://hdl.handle.net/10453/181366
dc.description.abstract	Water quality is paramount for the well-being of ecosystems and organisms, so assessing water quality variables (WQVs) is imperative. Despite existing research on predicting WQVs using machine learning models, none have tackled predicting WQVs at varying depths using surface water data. This study addresses this gap by collecting pertinent data and constructing an appropriate dataset. Utilizing AAQ-RINKO, a water quality profiling instrument, data was gathered from the Wadi Dayqah Dam reservoir in Oman. Leveraging surface water data (salinity, density, and temperature), the study estimates three WQVs - Dissolved Oxygen, Chlorophyll-a, and Turbidity - across depths ranging from 1 meter to 35 meters. A comprehensive evaluation of various machine learning and deep learning methods for tabular data was conducted alongside the introduction of seven ensemble approaches, with one emerging as the most effective. This approach leverages clustering to separate sub-models' importance in the final ensemble, enhanced by Bayesian optimization for weighting. Notably, Random Forest and Extreme Gradient Boosting techniques demonstrated superior performance following the proposed approach. Additional analyses assessed the impact of various inputs, sampling depth, time, and location. Overall, the proposed method exhibited significant enhancements, yielding improvements ranging from 4 % to 16 % based on the error metric.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Water Process Engineering
dc.relation.isbasedon	10.1016/j.jwpe.2024.105840
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0905 Civil Engineering, 0907 Environmental Engineering
dc.subject.classification	4004 Chemical engineering
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4011 Environmental engineering
dc.title	Integrated ensemble learning approach for multi-depth water quality estimation in reservoir environments
dc.type	Journal Article
utslib.citation.volume	66
utslib.for	0905 Civil Engineering
utslib.for	0907 Environmental Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	recently_added	*
dc.date.updated	2024-10-10T01:37:59Z
pubs.publication-status	Published
pubs.volume	66

Abstract:

Water quality is paramount for the well-being of ecosystems and organisms, so assessing water quality variables (WQVs) is imperative. Despite existing research on predicting WQVs using machine learning models, none have tackled predicting WQVs at varying depths using surface water data. This study addresses this gap by collecting pertinent data and constructing an appropriate dataset. Utilizing AAQ-RINKO, a water quality profiling instrument, data was gathered from the Wadi Dayqah Dam reservoir in Oman. Leveraging surface water data (salinity, density, and temperature), the study estimates three WQVs - Dissolved Oxygen, Chlorophyll-a, and Turbidity - across depths ranging from 1 meter to 35 meters. A comprehensive evaluation of various machine learning and deep learning methods for tabular data was conducted alongside the introduction of seven ensemble approaches, with one emerging as the most effective. This approach leverages clustering to separate sub-models' importance in the final ensemble, enhanced by Bayesian optimization for weighting. Notably, Random Forest and Extreme Gradient Boosting techniques demonstrated superior performance following the proposed approach. Additional analyses assessed the impact of various inputs, sampling depth, time, and location. Overall, the proposed method exhibited significant enhancements, yielding improvements ranging from 4 % to 16 % based on the error metric.

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http://hdl.handle.net/10453/181366