Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94301
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Deng, T | - |
dc.creator | Duan, HF | - |
dc.creator | Keramat, A | - |
dc.date.accessioned | 2022-08-11T02:01:44Z | - |
dc.date.available | 2022-08-11T02:01:44Z | - |
dc.identifier.issn | 1994-2060 | - |
dc.identifier.uri | http://hdl.handle.net/10397/94301 | - |
dc.language.iso | en | en_US |
dc.publisher | Hong Kong Polytechnic University, Department of Civil and Structural Engineering | en_US |
dc.rights | © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. | en_US |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.rights | The following publication Deng, T., Duan, H. F., & Keramat, A. (2022). Spatiotemporal characterization and forecasting of coastal water quality in the semi-enclosed Tolo Harbour based on machine learning and EKC analysis. Engineering Applications of Computational Fluid Mechanics, 16(1), 694-712 is available at https://doi.org/10.1080/19942060.2022.2035257 | en_US |
dc.subject | Coastal eutrophication | en_US |
dc.subject | EKC analysis | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Spatiotemporal analysis | en_US |
dc.subject | Tolo Harbour | en_US |
dc.title | Spatiotemporal characterization and forecasting of coastal water quality in the semi-enclosed Tolo Harbour based on machine learning and EKC analysis | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 694 | - |
dc.identifier.epage | 712 | - |
dc.identifier.volume | 16 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1080/19942060.2022.2035257 | - |
dcterms.abstract | Characterizing and forecasting coastal water quality and spatiotemporal evolution should be significant to coastal ecosystem management. However, high-quality modeling coastal water quality and their spatiotemporal evolutions is rather challenging due to complex dynamic mechanisms especially in a spatially and temporally heterogenous semi-enclosed bay. To this end, this study develops a framework incorporating machine learning (ML) algorithms and the Environmental Kuznets Curves (EKC) analysis to model, analyze and forecast the spatiotemporal variations of water quality indicators for different subzones and seasons in the semi-enclosed Tolo Harbour of Hong Kong. The application results indicate that the developed ML-based framework with an accuracy range of 0.672 ∼ 0.998 is well-suited in forecasting and understanding the coastal water evolution in a semi-enclosed harbour compared to conventional approach. Furthermore, the spatiotemporal characteristics of coastal water quality evolution in this semi-enclosed bay are analyzed and discussed for coastal hydro-environmental management. Moreover, the EKC analysis is also performed for determining the evolutions of essential water quality variables under 95% confidence interval of Hong Kong PCGDP projection and then implemented in the developed ML-based model for future prediction. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Engineering applications of computational fluid mechanics, 2022, v. 16, no. 1, p. 694-712 | - |
dcterms.isPartOf | Engineering applications of computational fluid mechanics | - |
dcterms.issued | 2022 | - |
dc.identifier.scopus | 2-s2.0-85125953023 | - |
dc.identifier.eissn | 1997-003X | - |
dc.description.validate | 202208 bckw | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a1604 | en_US |
dc.identifier.SubFormID | 45589 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
---|---|---|---|---|
19942060.2022.pdf | 6.94 MB | Adobe PDF | View/Open |
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