Deciphering Urban Flood Drivers: An Explainable Machine Learning Approach to Vulnerability Assessment in Indonesian Catchments

Domain:

environment and energygeospatial

Record type:

paper

Creator:

AhyGeoNurAnn

Publisher:

MDP

Host:

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.

Unveiling Urban Flood Vulnerability: A Machine Learning Approach for Mapping High Risk Zones in Tetouan City, Northern Morocco

This study examines urban flood vulnerability in Tetouan city, Northern Morocco, using four machine

Flood Vulnerability Assessment Using Geoinformation Data and Multicriteria Evolution Approach in Dakar Urban Environment, Senegal

During this last decade, urban flooding has become recurrent in Senegal, particularly in the city of

Wildfire Risk Assessment in Arid Oasis Ecosystems: An Integrated Machine Learning and Vulnerability Analysis Approach in Morocco.

Climate change is driving an alarming increase in wildfire frequency across arid ecosystems, highlig

Flood risk prediction and modeling in Bauchi: Leveraging machine learning models and explainable AI for urban resilience

From Climate Extremes to Risk: Quantifying Agro-Ecological Climate Vulnerability in Ghana Using Explainable Machine Learning

Climate extremes pose increasing risks to agro-ecological systems in sub-Saharan Africa, yet their d

Deciphering Urban Flood Drivers: An Explainable Machine Learning Approach to Vulnerability Assessment in Indonesian Catchments

Domain:

Record type:

Visit

Languages

Licenses

Similar

Unveiling Urban Flood Vulnerability: A Machine Learning Approach for Mapping High Risk Zones in Tetouan City, Northern Morocco

Flood Vulnerability Assessment Using Geoinformation Data and Multicriteria Evolution Approach in Dakar Urban Environment, Senegal

Wildfire Risk Assessment in Arid Oasis Ecosystems: An Integrated Machine Learning and Vulnerability Analysis Approach in Morocco.

Flood risk prediction and modeling in Bauchi: Leveraging machine learning models and explainable AI for urban resilience

From Climate Extremes to Risk: Quantifying Agro-Ecological Climate Vulnerability in Ghana Using Explainable Machine Learning

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm