Bi-LSTM and partial mutual information selection-based forecasting groundwater salinization levels

Muniappan, A; Jarin, T; Sabitha, R; Ghfar, AA; Rizwanul Fattah, IM; Bowa, CK; Mwanza, M

Bi-LSTM and partial mutual information selection-based forecasting groundwater salinization levels

Muniappan, A Jarin, T Sabitha, R Ghfar, AA Rizwanul Fattah, IM Bowa, CK Mwanza, M

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Publisher:: IWA PUBLISHING
Publication Type:: Journal Article
Citation:: Water Reuse, 2023, 13, (4), pp. 525-544
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Muniappan, A
dc.contributor.author	Jarin, T
dc.contributor.author	Sabitha, R
dc.contributor.author	Ghfar, AA
dc.contributor.author	Rizwanul Fattah, IM
dc.contributor.author	Bowa, CK
dc.contributor.author	Mwanza, M
dc.date.accessioned	2024-02-12T19:05:19Z
dc.date.available	2024-02-12T19:05:19Z
dc.date.issued	2023-12-01
dc.identifier.citation	Water Reuse, 2023, 13, (4), pp. 525-544
dc.identifier.issn	2709-6092
dc.identifier.issn	2709-6106
dc.identifier.uri	http://hdl.handle.net/10453/175596
dc.description.abstract	Fresh-saline groundwater is distributed in a highly heterogeneous way throughout the world. Groundwater salinization is a serious environmental issue that harms ecosystems and public health in coastal regions worldwide. Because of the complexities of groundwater salinization processes and the variables that influence them, it is challenging to predict groundwater salinity concentrations precisely. It compares cutting-edge machine learning (ML) algorithms for predicting groundwater salinity and identifying contributing factors. It employs bi-directional long short-term memory (BiLSTM) to indicate groundwater salinity. The input variable selection problem has attracted attention in the time series modeling community because it has been shown that information-theoretic input variable selection algorithms provide a more accurate representation of the modeled process than linear alternatives. To generate sample combinations for training multiple BiLSTM models, PMIS-selected predictors are used, and the predicted values from various BiLSTM models are also used to calculate the degree of prediction uncertainty for groundwater levels. The findings give policymakers insights for recommending groundwater salinity remediation and management strategies in the context of excessive groundwater exploitation in coastal lowland regions. To ensure sustainable groundwater management in coastal areas, it is essential to recognize the significant impact of human-caused factors on groundwater salinization.
dc.language	English
dc.publisher	IWA PUBLISHING
dc.relation.ispartof	Water Reuse
dc.relation.isbasedon	10.2166/wrd.2023.050
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Bi-LSTM and partial mutual information selection-based forecasting groundwater salinization levels
dc.type	Journal Article
utslib.citation.volume	13
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/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
dc.date.updated	2024-02-12T19:05:18Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	4

Abstract:

Fresh-saline groundwater is distributed in a highly heterogeneous way throughout the world. Groundwater salinization is a serious environmental issue that harms ecosystems and public health in coastal regions worldwide. Because of the complexities of groundwater salinization processes and the variables that influence them, it is challenging to predict groundwater salinity concentrations precisely. It compares cutting-edge machine learning (ML) algorithms for predicting groundwater salinity and identifying contributing factors. It employs bi-directional long short-term memory (BiLSTM) to indicate groundwater salinity. The input variable selection problem has attracted attention in the time series modeling community because it has been shown that information-theoretic input variable selection algorithms provide a more accurate representation of the modeled process than linear alternatives. To generate sample combinations for training multiple BiLSTM models, PMIS-selected predictors are used, and the predicted values from various BiLSTM models are also used to calculate the degree of prediction uncertainty for groundwater levels. The findings give policymakers insights for recommending groundwater salinity remediation and management strategies in the context of excessive groundwater exploitation in coastal lowland regions. To ensure sustainable groundwater management in coastal areas, it is essential to recognize the significant impact of human-caused factors on groundwater salinization.

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http://hdl.handle.net/10453/175596