Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study

Ghannam, S; Hussain, F

Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study

Ghannam, S Hussain, F

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2023, 275, pp. 110660
Issue Date:: 2023-09-05

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Field	Value	Language
dc.contributor.author	Ghannam, S
dc.contributor.author	Hussain, F https://orcid.org/0000-0003-1513-8072
dc.date.accessioned	2024-03-13T23:12:03Z
dc.date.available	2024-03-13T23:12:03Z
dc.date.issued	2023-09-05
dc.identifier.citation	Knowledge-Based Systems, 2023, 275, pp. 110660
dc.identifier.issn	0950-7051
dc.identifier.uri	http://hdl.handle.net/10453/176654
dc.description.abstract	Accurate water demand forecasting is essential for water utilities for effective water planning and pump scheduling. To conduct effective water planning and pump scheduling, Sydney Water needs a reliable method for predicting water demand. Short-term water demand forecasting deep learning- based models have been applied to different datasets collected from various water plants in the Greater Sydney region. This paper uses correlation analysis, the attention mechanism and a deep convolutional neural network to identify and study the importance of n number of factors that inform water prediction in the Greater Sydney region. The aim of this study is to develop, evaluate and compare multi- variate input deep learning-based models to predict water demand in various locations in the Greater Sydney region. This paper investigates five models — the recurrent neural network (RNN), long short- term memory networks (LSTM), bidirectional long short-term memory networks (BiLSTM), gated recurrent unit (GRU), and the deep convolutional neural network (CNN). The results of this study demonstrate that all the proposed models achieved a superior forecasting performance with accuracy hits of 97% using water consumption and climate factors as input data.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2023.110660
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4611 Machine learning
dc.title	Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study
dc.type	Journal Article
utslib.citation.volume	275
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	in_progress	*
dc.date.updated	2024-03-13T23:11:58Z
pubs.publication-status	Published
pubs.volume	275

Abstract:

Accurate water demand forecasting is essential for water utilities for effective water planning and pump scheduling. To conduct effective water planning and pump scheduling, Sydney Water needs a reliable method for predicting water demand. Short-term water demand forecasting deep learning- based models have been applied to different datasets collected from various water plants in the Greater Sydney region. This paper uses correlation analysis, the attention mechanism and a deep convolutional neural network to identify and study the importance of n number of factors that inform water prediction in the Greater Sydney region. The aim of this study is to develop, evaluate and compare multi- variate input deep learning-based models to predict water demand in various locations in the Greater Sydney region. This paper investigates five models — the recurrent neural network (RNN), long short- term memory networks (LSTM), bidirectional long short-term memory networks (BiLSTM), gated recurrent unit (GRU), and the deep convolutional neural network (CNN). The results of this study demonstrate that all the proposed models achieved a superior forecasting performance with accuracy hits of 97% using water consumption and climate factors as input data.

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http://hdl.handle.net/10453/176654