Community Fault Reporting Model for the Proactive Disaster Management of Environmental Risks

Abstract

Environmental risks, such as infrastructure damage and environmental issues like potholes, overgrown trees, clogged drains, and broken streetlights, pose significant threats to public safety. This paper proposes the Community Fault Reporting Conceptual Model, named Urban Alert!, to improve environmental risk management through community engagement and data analytics in Malaysia. The model integrates geo-tagged, multi-platform community reporting with a data pipeline, followed by Business Intelligence (BI) and Machine Learning (ML) analytics to support the identification of recurring and risk-prone areas, enabling risk prioritization and early intervention by responsible authorities to implement proactive measures. The software prototype architecture and user interaction model are also presented in this paper. This study concentrates on the evaluation of architectural validation, prototype implementation, and preliminary analytic capabilities rather than extensive empirical deployment. It also improves the community fault reporting model by improving community data reporting, location tracking, data processing and cleansing, integrated with predictive analysis for proactive disaster management. This study contributes to SDG 11, Sustainable Cities and Communities that aims to make cities and human settlements inclusive, safe, resilient and sustainable by improving fault reporting in proactive disaster management.