Integration of AI and GIS in Clean Water Quality Monitoring in Urban and Rural Communities - Sudasman, Fuad Hilmi; Gereuw, Gabriel Enjel (RIGGS: Journal of Artificial Intelligence and Digital Business, 2025)
Ensuring clean water quality remains a critical challenge for public health and sustainable development. Conventional monitoring methods, which rely on manual sampling and laboratory tests, often fall short in covering large areas, responding quickly, or operating efficiently. This systematic review explores how emerging technologies—namely Artificial Intelligence (AI), Geographic Information Systems (GIS), IoT sensors, and remote sensing (via satellite and UAVs)—are being used to enhance water quality monitoring in both urban and rural settings. Based on 10 empirical studies from 2010 to 2025, findings show that AI models like Random Forest, SVM, CNN, and LSTM can predict water quality indicators such as DO, BOD, COD, and WQI with over 90% accuracy. GIS supports spatial mapping and risk analysis, while integration with real-time sensors and community-based approaches like Participatory GIS (PGIS) improves relevance and responsiveness. Still, issues such as infrastructure gaps, low digital literacy, limited public engagement, and opaque AI systems hinder wider adoption. The review highlights the need for inclusive, flexible, and policy-supported AI-GIS frameworks to transform water monitoring into a more predictive, participatory, and context-aware process.