Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters

Tianan Deng; Huan Feng Duan; Jinghua Wang; Alireza Keramat

Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters

Tianan Deng, Huan Feng Duan, Jinghua Wang, Alireza Keramat

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Water quality inversion model based on satellite images could provide a good complement for coastal hydro-environment monitoring in the Hong Kong waters. However, previous studies attempted to establish a single inversion model for expressing the whole Hong Kong waters without considering the regional differences of water properties. In this study, the whole Hong Kong waters is firstly divided into 6 clusters for the inspection of their respective water quality characteristics. The local models for a specific cluster are then developed and compared with the original single model results for the Hong Kong water quality analysis. It shows that the results obtained by the cluster-based local inversion models are comparable to or even better than those by the original single models, thereby providing a more effective tool for the coastal water quality management in Hong Kong.

Original language	English
Title of host publication	Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023
Editors	Jin S. Chung, Decheng Wan, Satoru Yamaguchi, Shiqiang Yan, Igor Buzin, Hiroyasu Kawai, Hua Liu, Ivana Kubat, Bor-Feng Peng, Ali Reza, Venkatachalam Sriram, Suak Ho Van
Publisher	International Society of Offshore and Polar Engineers
Pages	1096-1102
Number of pages	7
ISBN (Print)	9781880653807
Publication status	Published - Jun 2023
Event	33rd International Ocean and Polar Engineering Conference, ISOPE 2023 - Ottawa, Canada Duration: 19 Jun 2023 → 23 Jun 2023

Publication series

Name	Proceedings of the International Offshore and Polar Engineering Conference
ISSN (Print)	1098-6189
ISSN (Electronic)	1555-1792

Conference

Conference	33rd International Ocean and Polar Engineering Conference, ISOPE 2023
Country/Territory	Canada
City	Ottawa
Period	19/06/23 → 23/06/23

Keywords

Google Earth Engine
Self-organizing map
Water quality inversion models
Xgboost

ASJC Scopus subject areas

Energy Engineering and Power Technology
Ocean Engineering
Mechanical Engineering

Cite this

Deng, T., Duan, H. F., Wang, J., & Keramat, A. (2023). Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters. In J. S. Chung, D. Wan, S. Yamaguchi, S. Yan, I. Buzin, H. Kawai, H. Liu, I. Kubat, B.-F. Peng, A. Reza, V. Sriram, & S. H. Van (Eds.), Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023 (pp. 1096-1102). (Proceedings of the International Offshore and Polar Engineering Conference). International Society of Offshore and Polar Engineers.

Deng, Tianan ; Duan, Huan Feng ; Wang, Jinghua et al. / Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters. Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023. editor / Jin S. Chung ; Decheng Wan ; Satoru Yamaguchi ; Shiqiang Yan ; Igor Buzin ; Hiroyasu Kawai ; Hua Liu ; Ivana Kubat ; Bor-Feng Peng ; Ali Reza ; Venkatachalam Sriram ; Suak Ho Van. International Society of Offshore and Polar Engineers, 2023. pp. 1096-1102 (Proceedings of the International Offshore and Polar Engineering Conference).

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title = "Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters",

abstract = "Water quality inversion model based on satellite images could provide a good complement for coastal hydro-environment monitoring in the Hong Kong waters. However, previous studies attempted to establish a single inversion model for expressing the whole Hong Kong waters without considering the regional differences of water properties. In this study, the whole Hong Kong waters is firstly divided into 6 clusters for the inspection of their respective water quality characteristics. The local models for a specific cluster are then developed and compared with the original single model results for the Hong Kong water quality analysis. It shows that the results obtained by the cluster-based local inversion models are comparable to or even better than those by the original single models, thereby providing a more effective tool for the coastal water quality management in Hong Kong.",

keywords = "Google Earth Engine, Self-organizing map, Water quality inversion models, Xgboost",

author = "Tianan Deng and Duan, {Huan Feng} and Jinghua Wang and Alireza Keramat",

note = "Publisher Copyright: {\textcopyright} 2023 by the International Society of Offshore and Polar Engineers (ISOPE).; 33rd International Ocean and Polar Engineering Conference, ISOPE 2023 ; Conference date: 19-06-2023 Through 23-06-2023",

year = "2023",

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language = "English",

isbn = "9781880653807",

series = "Proceedings of the International Offshore and Polar Engineering Conference",

publisher = "International Society of Offshore and Polar Engineers",

pages = "1096--1102",

editor = "Chung, {Jin S.} and Decheng Wan and Satoru Yamaguchi and Shiqiang Yan and Igor Buzin and Hiroyasu Kawai and Hua Liu and Ivana Kubat and Bor-Feng Peng and Ali Reza and Venkatachalam Sriram and Van, {Suak Ho}",

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Deng, T, Duan, HF, Wang, J & Keramat, A 2023, Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters. in JS Chung, D Wan, S Yamaguchi, S Yan, I Buzin, H Kawai, H Liu, I Kubat, B-F Peng, A Reza, V Sriram & SH Van (eds), Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023. Proceedings of the International Offshore and Polar Engineering Conference, International Society of Offshore and Polar Engineers, pp. 1096-1102, 33rd International Ocean and Polar Engineering Conference, ISOPE 2023, Ottawa, Canada, 19/06/23.

Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters. / Deng, Tianan; Duan, Huan Feng; Wang, Jinghua et al.
Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023. ed. / Jin S. Chung; Decheng Wan; Satoru Yamaguchi; Shiqiang Yan; Igor Buzin; Hiroyasu Kawai; Hua Liu; Ivana Kubat; Bor-Feng Peng; Ali Reza; Venkatachalam Sriram; Suak Ho Van. International Society of Offshore and Polar Engineers, 2023. p. 1096-1102 (Proceedings of the International Offshore and Polar Engineering Conference).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters

AU - Deng, Tianan

AU - Duan, Huan Feng

AU - Wang, Jinghua

AU - Keramat, Alireza

PY - 2023/6

Y1 - 2023/6

N2 - Water quality inversion model based on satellite images could provide a good complement for coastal hydro-environment monitoring in the Hong Kong waters. However, previous studies attempted to establish a single inversion model for expressing the whole Hong Kong waters without considering the regional differences of water properties. In this study, the whole Hong Kong waters is firstly divided into 6 clusters for the inspection of their respective water quality characteristics. The local models for a specific cluster are then developed and compared with the original single model results for the Hong Kong water quality analysis. It shows that the results obtained by the cluster-based local inversion models are comparable to or even better than those by the original single models, thereby providing a more effective tool for the coastal water quality management in Hong Kong.

AB - Water quality inversion model based on satellite images could provide a good complement for coastal hydro-environment monitoring in the Hong Kong waters. However, previous studies attempted to establish a single inversion model for expressing the whole Hong Kong waters without considering the regional differences of water properties. In this study, the whole Hong Kong waters is firstly divided into 6 clusters for the inspection of their respective water quality characteristics. The local models for a specific cluster are then developed and compared with the original single model results for the Hong Kong water quality analysis. It shows that the results obtained by the cluster-based local inversion models are comparable to or even better than those by the original single models, thereby providing a more effective tool for the coastal water quality management in Hong Kong.

KW - Google Earth Engine

KW - Self-organizing map

KW - Water quality inversion models

KW - Xgboost

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M3 - Conference article published in proceeding or book

AN - SCOPUS:85188674050

SN - 9781880653807

T3 - Proceedings of the International Offshore and Polar Engineering Conference

SP - 1096

EP - 1102

BT - Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023

A2 - Chung, Jin S.

A2 - Wan, Decheng

A2 - Yamaguchi, Satoru

A2 - Yan, Shiqiang

A2 - Buzin, Igor

A2 - Kawai, Hiroyasu

A2 - Liu, Hua

A2 - Kubat, Ivana

A2 - Peng, Bor-Feng

A2 - Reza, Ali

A2 - Sriram, Venkatachalam

A2 - Van, Suak Ho

PB - International Society of Offshore and Polar Engineers

T2 - 33rd International Ocean and Polar Engineering Conference, ISOPE 2023

Y2 - 19 June 2023 through 23 June 2023

ER -

Deng T, Duan HF, Wang J, Keramat A. Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters. In Chung JS, Wan D, Yamaguchi S, Yan S, Buzin I, Kawai H, Liu H, Kubat I, Peng BF, Reza A, Sriram V, Van SH, editors, Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023. International Society of Offshore and Polar Engineers. 2023. p. 1096-1102. (Proceedings of the International Offshore and Polar Engineering Conference).

Effective cluster-based coastal water quality inversion modeling and analysis for Hong Kong waters

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Keywords

ASJC Scopus subject areas

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