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GEL-IoT: Geospatially Enabled Learning approaches for Intelligent IoT based water quality monitoring

Dept/Center/Lab: Amrita Center for Wireless Networks and Applications (AWNA)

Project Incharge:Dr. Aryadevi R. D.
Co-Project Incharge:Divya SJ (Research Assistant), Ganesh Narayan(Research Associate), Krishnendu (Project Assistant), Dr. P. S. Harikumar, Senior principal Scientist, and Head, Ecology and Environment Research Group, Centre for Water Resources Development and Management (CWRDM)
GEL-IoT: Geospatially Enabled Learning approaches for Intelligent IoT based water quality monitoring

Water, essential for life, is jeopardized in developing areas, impacting human health and ecosystems. Our mission, focused on Kuttanad, Varapuzha, and Kulasekharapuram in Kerala, pioneers remote sensing and IoT for dynamic monitoring of water sources. Continuous assessment and mapping aim to address waterborne diseases and enhance water security in vulnerable communities.Access to clean drinking water is a fundamental human need essential for survival and the health of ecosystems. However, in many developing countries, the reliance on contaminated water sources has led to a surge in waterborne diseases, posing a severe public health threat. Contributing to this crisis are factors such as rapid urbanization, population growth, and unpredictable weather patterns, which exacerbate the challenges of accessing clean and safe drinking water solutions. Shockingly, more than half of the world’s communities still lack access to reliable clean water sources. The absence of real-time data on water quality further compounds the problem, making it difficult to assess and manage health risks associated with water contamination effectively. Monitoring multiple water sources across communities is crucial for understanding the dynamic variability in contamination levels and devising targeted interventions to safeguard public health. To address these challenges, innovative remote sensing technologies offer a promising solution. By harnessing remote sensing-based techniques, continuous monitoring and mapping of water sources can be achieved, providing invaluable insights into water quality dynamics. These techniques enable the development of community-centric health risk assessment models, empowering decision-makers with the information needed to implement timely interventions and ensure the delivery of clean drinking water to vulnerable communities.

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