Time series (2003–15) analysis of selected physicochemical parameters in Indian Ocean: cumulative impacts prediction on coral bleaching using machine learning

Highlights

• Long term prediction of coral bleaching in Indian Ocean
• Cumulative impacts of selected physico chemicals parameters on bleaching
• Application of machine learning in environmental impacts study
• Salinity fCO2 and pH have positive impacts on coral bleaching.

Abstract

Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters like pH, tCO₂, fCO₂, salinity, dissolved oxygen, etc. also play a significant role in bleaching. In the present study, all these parameters of the Indian Ocean area for 15 years (2003–2017) were collected and analysed using machine learning language. The main aim is to see the cumulative impacts of various ocean parameters on coral bleaching. Introducing machine learning in environmental impact assessment studies is a new approach, and the prediction of coral bleaching using simulation of physico-chemical parameters interactions shows 70 % accuracy for the prediction of the future bleaching event. This study can be probably the first step in the application of the machine learning language for the prediction of coral bleaching in the field of marine science.

Panja A. P., Jaiswal S. & Haldar S., 2024. Time series (2003–15) analysis of selected physicochemical parameters in Indian Ocean: Cumulative impacts prediction on coral bleaching using machine learning. Science of the Total Environment: 173002. doi: 10.1016/j.scitotenv.2024.173002. Article.

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