Development of a web application through a mobilized crowdsourcing platform to enable participatory risk sensitive urban development

Abstract

Flooding remains the most frequent and destructive natural disaster in Sri Lanka, exacerbated by rapid urbanization and evolving precipitation patterns, leading to extensive socio-economic damage. Despite the availability of technological tools for disaster management, community engagement in early warning systems and risk mitigation remains minimal. This study addresses this gap by developing an integrated platform comprising a crowdsourcing-based mobile application and a web-based geospatial decision support system, aimed at fostering participatory risk-sensitive urban development. The mobile application enables real-time flood data collection from affected communities, while the web platform visualizes and validates this data, providing actionable insights for disaster response authorities. A flood vulnerability assessment model was developed using 30 years of historical flood data, nine key conditioning factors—including topography, weather patterns, hydrology, land cover, and soil type—and Sentinel-2 satellite imagery to enhance prediction accuracy. The methodological approach integrates machine learning techniques for crowdsourced data verification, participatory workshops for system validation, and mobilization strategies for community engagement. The research makes significant contributions to both theory and practice. Theoretically, it advances the discourse on participatory disaster management by integrating community-based crowdsourcing with remote sensing analytics. The study also refines geospatial modelling techniques for flood vulnerability assessment, incorporating novel indicators such as Night-Time Light (NTL) data to measure human exposure to flooding. In practice, the developed platform enhances disaster preparedness by providing a scalable, cost-effective solution for real-time flood risk assessment. The findings demonstrate that combining crowdsourced data with remote sensing can bridge critical information gaps in disaster management, empowering communities and authorities to respond proactively to flood hazards. The study's framework can be adapted to other flood-prone regions, contributing to global efforts in urban resilience and risk-sensitive development.