AI-integrated GIS and remote sensing for flood management in Malaysian construction

Flood disasters are an increasing challenge for construction projects in Malaysia, particularly in flood-prone regions where climate variability, land-use change, and limited preparedness heighten site vulnerability. This study examines how Geographic Information Systems (GIS) and Remote Sensing (RS) can support flood disaster management in construction projects and proposes an enhanced framework incorporating artificial intelligence (AI)-enabled analytical capabilities. A quantitative cross-sectional survey was conducted among Grade G7 contractors in Kelantan, Terengganu, and Pahang. From a population of 547 firms, 241 valid responses were obtained and analysed using mean score and ranking analysis in IBM SPSS. Findings indicate that flood disaster management is constrained by weak planning readiness, limited access to accurate flood-related information, and insufficient adoption of advanced digital tools. Respondents recognised GIS and RS as valuable for flood risk mapping, terrain assessment, real-time monitoring, and decision making. Based on these findings, an empirically informed AI–GIS–RS framework is proposed. Although AI models were not directly developed or tested, the framework conceptually incorporates AI for flood-susceptibility prediction, automated flood-extent detection, and optimisation-oriented decision support. This study provides a practical, construction-focused pathway to strengthen project resilience, operational continuity, and sustainable project delivery in flood-prone regions.