Work

AI-Driven Time Series Forecasting of Coastal Water Quality Using Sentinel-2 Imagery: A Case Study in the Gulf of Thailand

Year: 2025

Type: article

Abstract: The accurate prediction of water quality parameters is essential for effective pollution control and resource management. This study presents a hybrid AI-remote sensing framework for forecasting water quality in the Gulf of Thailand, which combines Sentinel-2 imagery with Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) models. Our approach achieves a 5.4× increase in data coverage over traditional methods, demonstrating the effectiveness of machine learning in environmental monitoring. Predictive accuracy was evaluated across Support Vector Machine (SVM), ARIMA, and Amazon Forecast models. Results indicate that SVM, optimised through RBF kernel and grid search, outperforms other models for Chlorophyll-a (RMSE: 1.8), while ARIMA exhibits superior performance for Secchi Depth (RMSE: 0.2) and Trophic State Index (RMSE: 0.8). The study also introduces Aqua Sight, a web-based visualisation tool built on Google Earth Engine, enabling stakeholders to access real-time water quality forecasts. These findings highlight the potential of integrating satellite-derived data with machine learning to enhance early warning systems and support environmental decision making in coastal ecosystems.

Source: Water

DOI: 10.3390/w17121798

Authors: Arsanchai Sukkuea, Pensiri Akkajit, Korakot Suwannarat, Punnawit Foithong, Nasrin Afsarimanesh, Md Eshrat E. Alahi

Institutions: Walailak University, Prince of Songkla University, Curtin University

Language: English

Topic: Water Quality Monitoring Technologies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Climate action

Open Access status: gold

Estimated APC paid: $3,137