Integration of AI and GIS in Clean Water Quality Monitoring in Urban and Rural Communities

Fuad Hilmi Sudasman; Gabriel Enjel Gereuw

doi:10.31004/riggs.v4i2.1425

Authors

Fuad Hilmi Sudasman Universitas Negeri Manado
Gabriel Enjel Gereuw Universitas Negeri Manado

DOI:

https://doi.org/10.31004/riggs.v4i2.1425

Keywords:

water quality, Artificial Intelligence, GIS, environmental monitoring, urban and rural communities, IoT

Abstract

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.

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References

Agyapong, A., Oduro Kwarteng, S., & Appiah Effah, E. (2021). Participatory GIS for rural water quality monitoring in Ghana. Water Policy, 23(4), 789–804.

Goodchild, M. F. (2009). Geographic information systems and science: today and tomorrow. Annals of GIS, 15(1), 3–9. DOI: 10.1080/19475680903250715

Gray, K. M. (2018). From Content Knowledge to Community Change: A Review of Representations of Environmental Health Literacy. International Journal of Environmental Research and Public Health, 15(3), 466. DOI: 10.3390/ijerph15030466

Ministry of Health of the Republic of Indonesia. (2023). Indonesia Health Profile 2022. Jakarta: Indonesian Ministry of Health.

Khalil, R. A., Saeed, R. M., & Ahmed, H. (2022). Predictive modeling of water quality using LSTM and IoT sensors in Nile River. Journal of Water and Climate Change, 13(2), 320–334.

Kourgialas, N. N., Dokou, Z., & Karatzas, G. P. (2018). Water quality modeling using GIS and statistical methods. Environmental Monitoring and Assessment, 190(1), 45.

Li, H., Sun, X., & Li, L. (2020). A review of artificial intelligence applications in water quality monitoring. Water, 12(7), 1995. DOI: 10.3390/w12071995

Li, L., Zhang, J., & Zhao, Y. (2021). Integrating remote sensing and IoT for water quality assessment in lakes. Sensors, 21(18), 6209.

Liu, Y., Wang, S., & Zhang, X. (2016). Deep learning based urban water quality mapping with GIS and environmental data. Journal of Environmental Informatics, 27(1), 25–34.

Malik, R., Awan, M. S., & Javed, S. (2023). CNN and GIS integration for urban river pollution mapping. Environmental Technology & Innovation, 29, 102997.

McCall, M. K., & Dunn, C. E. (2012). Geo information tools for participatory spatial planning: Fulfilling the criteria for 'good' governance? Geoforum, 43(1), 81–94.

Oliveira, R., Silva, P., & Costa, A. (2024). Real time urban water quality monitoring with AI and GIS in smart cities. Smart Water, 9(2), 112–126.

Ras, G., van Gerven, M., & Haselager, P. (2018). Explainable AI: From black box to interpretable models. Nature Machine Intelligence, 1(1), 10–11. DOI: 10.1038/s42256 018 0001 z

Ravikumar, P., & Somashekar, R. K. (2020). GIS based assessment of groundwater contamination in India using DRASTIC model. Environmental Earth Sciences, 79(12), 1–12. DOI: 10.1007/s12665 020 08959 1

Sunaryo, S., Widodo, D. S., & Lestari, A. P. (2020). GIS based water quality monitoring in industrial zones of Indonesia using random forest. International Journal of Environmental Science and Technology, 17(10), 4201–4214.

United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development.

WHO & UNICEF. (2021). Progress on household drinking water, sanitation and hygiene 2000–2020: Five years into the SDGs.

Wu, J., Zhang, Y., & Chen, M. (2022). Integration of remote sensing, IoT and AI for real time water quality monitoring: A review. Environmental Monitoring and Assessment, 194(6), 446.

Zhang, Y., Liu, C., & Wang, R. (2021). A hybrid model using GIS and machine learning for water quality prediction in river networks. Journal of Hydrology, 596, 126071.

Zhang, X., Li, Y., & Zhao, F. (2022). Machine learning for river water quality prediction: A review. Water Research, 212, 118104.