A Machine Learning Approach to Predict Urban Flood Risk under Future Climate Change Scenarios: A Case Study of Jakarta, Indonesia

Jakarta, a rapidly urbanizing coastal city, faces increasing flood risks because of climate change. Many studies have analyzed the city’s flood risk, but they have mostly relied on surveys or aggregated data, limiting the assessment of persistent flood risks at finer spatial scales. This study develops a Flood Risk Index (FRI) to assess and predict flood risks in Jakarta, a highly flood-prone city, under projected climate change scenarios for 2050. By integrating physical and meteorological fact