Prediction of Water Quality in Reservoirs: A Comparative Assessment of Machine Learning and Deep Learning Approaches in the Case of Toowoomba, Queensland, Australia

Article


Farzana, Syeda Zehan, Paudyal, Dev Raj, Chadalavada, Sreeni and Alam, Md Jahangir. 2023. "Prediction of Water Quality in Reservoirs: A Comparative Assessment of Machine Learning and Deep Learning Approaches in the Case of Toowoomba, Queensland, Australia." Geosciences. 13 (293). https://doi.org/10.3390/geosciences13100293
Article Title

Prediction of Water Quality in Reservoirs: A Comparative Assessment of Machine Learning and Deep Learning Approaches in the Case of Toowoomba, Queensland, Australia

ERA Journal ID210512
Article CategoryArticle
AuthorsFarzana, Syeda Zehan, Paudyal, Dev Raj, Chadalavada, Sreeni and Alam, Md Jahangir
Journal TitleGeosciences
Journal Citation13 (293)
Article Number293
Number of Pages24
Year2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2076-3263
Digital Object Identifier (DOI)https://doi.org/10.3390/geosciences13100293
Web Address (URL)https://www.mdpi.com/2076-3263/13/10/293
Abstract

The effective management of surface water bodies, such as rivers, lakes, and reservoirs, necessitates a comprehensive understanding of water quality status. Altered precipitation patterns due to climate change may significantly affect the water quality and influence treatment procedures.
This study aims to identify the most suitable water quality prediction models for the assessment of the water quality status for three water supply reservoirs in Toowoomba, Australia. It employed four machine learning and two deep learning models for determining the Water Quality Index (WQI) based on five parameters sensitive to rainfall impact. Temporal WQI variations over a period of 22 years (2000–2022) are scrutinised across 4 seasons and 12 months. Through regression analysis, both machine learning and deep learning models anticipate WQI gauged by seven accuracy metrics. Notably, XGBoost and GRU yielded exceptional outcomes, showcasing an R2 value of 0.99. Conversely, Bidirectional LSTM (BiLSTM) demonstrated moderate accuracy with results hovering at 88% to 90% for water quality prediction across all reservoirs. The Coefficient of Efficiency (CE) and Willmott Index (d) showed that the models capture patterns well, while MAE, MAPE and RMSE provided good performance metrics for the RFR, XGBoost and GRU models. These models have provided valuable knowledge that can be utilised to assess the adverse consequences of extreme climate events such as shifts in rainfall patterns. These insights can be used to improve strategies for managing water bodies more effectively.

Keywordswater quality index; variation in water quality index; real-time monitoring; machine learning; deep learning
Article Publishing Charge (APC) Amount Paid1500.0
Article Publishing Charge (APC) FundingResearcher
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400513. Water resources engineering
401302. Geospatial information systems and geospatial data modelling
461103. Deep learning
461199. Machine learning not elsewhere classified
Byline AffiliationsSchool of Engineering
School of Surveying and Built Environment
Murray-Darling Basin Authority, Australia
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