Smart Environmental Monitoring: Leveraging Remote Sensing and Machine Learning for Water Quality Assessment at Da Nang Beaches

Da Nang, renowned for its tourism centered around beautiful beaches, faces the critical challenge of maintaining high-quality coastal waters. Traditional in-situ monitoring methods, though valuable, often lack sufficient spatial coverage, which is particularly problematic for such a significant urban area. Our study pioneers an innovative approach to beach water quality monitoring in Da Nang by utilizing remote sensing technology, specifically Sentinel-2 imagery, across four key local beaches. By integrating sophisticated machine learning algorithms and improving their classification precision from previous works, our methodology offers a scalable and cost-effective solution for the precise assessment of essential water quality parameters, including salinity, turbidity, and chlorophyll-a levels. Through feature importance analysis of the existing in-situ water quality dataset, we identify chlorophyll-a as a highly significant indicator of water quality, likely due to its correlation with algal blooms and nutrient levels. Our results demonstrate that the overall water quality at all Da Nang beaches was consistently good during the satellite data collection period in March 2024. The implications of this research are far-reaching, advancing smart city initiatives, promoting sustainable tourism, enhancing environmental stewardship, and supporting resilient urban development in coastal settings.