Research Article
AquaMap: Empowering Communities to Report and Map Water-Related Issues in Real-Time with Deep Learning
@INPROCEEDINGS{10.1007/978-3-031-77075-3_24, author={Harshitha Lakshmi Durga Nalla and Anusha Bhuchupalli and Tejasree Addala and Yasasri Sabbineni and Koppisetti Sravya Geetha and Ghantasala Aasha and Sridevi Bonthu}, title={AquaMap: Empowering Communities to Report and Map Water-Related Issues in Real-Time with Deep Learning}, proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I}, proceedings_a={IC4S}, year={2025}, month={2}, keywords={Water-related issues Disaster response Sustainable Development Deep Learning Custom Data web application}, doi={10.1007/978-3-031-77075-3_24} }- Harshitha Lakshmi Durga Nalla
Anusha Bhuchupalli
Tejasree Addala
Yasasri Sabbineni
Koppisetti Sravya Geetha
Ghantasala Aasha
Sridevi Bonthu
Year: 2025
AquaMap: Empowering Communities to Report and Map Water-Related Issues in Real-Time with Deep Learning
IC4S
Springer
DOI: 10.1007/978-3-031-77075-3_24
Abstract
In today’s era of rapid urbanization and environmental challenges, effective disaster and crisis management demand innovative solutions. This paper presents a novel approach focusing on community-level water-related issues through an intelligently designed application. The primary objective is to develop a user-friendly platform facilitating the reporting of water-related problems by both social media posts and the general public, subsequently enabling prompt action by relevant government authorities. Leveraging deep learning techniques and user-generated data, our solution introduces real-time detection and classification of six distinct water-related problems. A custom dataset is curated to train a ResNet-18 model, achieving an impressive accuracy of 73%. The application, developed with a React-based frontend and Flask-powered backend, acts as a centralized hub for reporting and managing water issues. Notably, it employs user-inputted data to accurately pinpoint problem locations, thereby enhancing the precision of reporting. By presenting a holistic approach, this research significantly contributes to the development of efficient crisis management and response strategies for water-related disasters.
