Machine learning based marine water quality prediction for coastal hydro-environment management
| Autor: | Kwok Wing Chau, Huan Feng Duan, Tianan Deng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Conservation of Natural Resources
Environmental Engineering Harmful Algal Bloom 0208 environmental biotechnology 02 engineering and technology 010501 environmental sciences Management Monitoring Policy and Law Machine learning computer.software_genre 01 natural sciences Algal bloom Machine Learning Water Quality Water environment Robustness (economics) Waste Management and Disposal 0105 earth and related environmental sciences computer.programming_language Artificial neural network business.industry General Medicine 020801 environmental engineering Support vector machine Harbour Environmental science Hong Kong Water quality Artificial intelligence business Eutrophication computer Environmental Monitoring |
| Zdroj: | Journal of environmental management. 284 |
| ISSN: | 1095-8630 |
| Popis: | During the past three decades, harmful algal blooms (HAB) events have been frequently observed in marine waters around many coastal cities in the world including Hong Kong. The increasing occurrence of HAB has caused acute influences and damages on water environment and marine aquaculture with millions of monetary losses. For example, the Tolo Harbour is one of the most affected areas in Hong Kong, where more than 30% HAB occurred. In order to forewarn the potential HAB incidents, the machine learning (ML) methods have been increasingly resorted in modelling and forecasting water quality issues. In this study, two different ML methods - artificial neural networks (ANN) and support vector machine (SVM) - are implemented and improved by introducing different hybrid learning algorithms for the simulations and comparative analysis of more than 30-year measured data, so as to accurately forecast algal growth and eutrophication in Tolo Harbour in Hong Kong. The application results show the good applicability and accuracy of these two ML methods for the predictions of both trend and magnitude of the algal growth. Specifically, the results reveal that ANN is preferable to achieve satisfactory results with quick response, while the SVM is suitable to accurately identify the optimal model but taking longer training time. Moreover, it is demonstrated that the used ML methods could ensure robustness to learn complicated relationship between algal dynamics and different coastal environmental variables and thereby to identify significant variables accurately. The results analysis and discussion of this study also indicate the potentials and advantages of the applied ML models to provide useful information and implications for understanding the mechanism and process of HAB outbreak and evolution that is helpful to improving the water quality prediction for coastal hydro-environment management. |
| Databáze: | OpenAIRE |
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