Flooding is one of the most frequent and damaging natural disasters, accounting for nearly half of global disasters and posing a major challenge in Indonesia, where floods represent approximately 77% of all nationally recorded disaster events. Rapid urbanisation, land-use change, and climate-induced extreme rainfall have intensified flood risks nationwide. However, existing vulnerability assessments remain fragmented and localised, limiting their relevance for national-scale adaptation planning. This study develops a measurable and explainable framework for assessing urban flood vulnerability across Indonesia using cloud-based geospatial data and interpretable machine learning. The approach integrates CEMS-GLOFAS (flood hazard), WorldPop (population exposure), SRTM (topography), and ESA WorldCover (land cover) datasets within Google Earth Engine (GEE). Flood vulnerability is quantified through a modified Flood Vulnerability Index (FVI) combining hazard, exposure, and physical vulnerability components. The Extreme Gradient Boosting (XGBoost) model predicts FVI values, while SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) enhance model transparency and identify the influence of key variables such as flood depth, population density, and elevation. The model achieved high predictive accuracy (R² = 0.89; RMSE = 0.04728 FVI units, dimensionless) and revealed substantial spatial heterogeneity across 514 districts, with the highest FVI (0.75–0.85) in Banda Aceh, Mojokerto, Pasuruan, Samarinda, and Merauke. The integration of GEE and explainable AI offers a transparent, scalable framework to support data-driven flood risk mitigation and urban climate resilience in Indonesia.

The increasing frequency of climate change and extreme weather makes the issue of urban flood vulnerability an important one. Flood disaster mitigation efforts have progressed rapidly with the utilization of technology in flood risk assessment and community preparedness. However, there is still a gap between technology and community engagement in preparedness. This study was conducted to analyze the trend of technology-based urban flood vulnerability and community preparedness integration. The study used bibliometric methods to see research trends and a systematic review of Scopus data with Biblioshiny, VOSviewer, and Convidence to identify relevant research articles. Trends in urban flood vulnerability show an increasing use of advanced technologies such as multivariate LSTM (Long Short-Term Memory) artificial neural networks, LiDAR (Light Detection and Ranging), GIS (Geographic Information System), and blockchain for flood risk assessment. There are gaps in community engagement and preparedness, highlighting the need for a comprehensive approach that combines technology with community-based strategies. A balance between technology implementation and community engagement is needed to improve community preparedness and safeguard water security in flood-prone urban areas. The development of an urban flood vulnerability index can bridge the technology gap with standardized community engagement. All the articles in the study contribute greatly to the changes and improvements in flood disaster mitigation, as well as to the role of technology and preparedness. The gap between technology and community engagement in preparedness requires an urban flood vulnerability index with comprehensive strategies.