Spatiotemporal characterization and forecasting of coastal water quality in the semi-enclosed Tolo Harbour based on machine learning and EKC analysis

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Pages (from-to)694-712
Number of pages19
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 2022


  • Coastal eutrophication
  • EKC analysis
  • machine learning
  • spatiotemporal analysis
  • Tolo Harbour

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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