Review article: Hydrologically Enhanced Machine Learning Framework for Urban Flood Inundation Mapping Using Multi-Sensor Remote Sensing Data: A Case Study of Mumbai, India

Abstract. The complicated terrain, highly populated building surfaces and insufficient credible ground observations make urban flood mapping difficult in urbanizing megacities that rapidly develop in coastal areas. This study suggests that a hydrologically improved machine learning architecture can be utilized to perform automated urban flood inundation mapping by combining multi-sensor satellite data with a scalable decision support system (DSS). The Google Earth engine used Sentinel-1 SAR, Sentinel-2 optical imagery, SRTM digital elevation data, and CHIRPS precipitation data to create a comprehensive predictor stack.

To explicitly model flood propagation controls that most data-driven models tend to omit, two new hydrologic-topographic predictors were created:-the Relative Elevation Model (REM) and River Network Index (RNI), to model local terrain depressions and hydraulic connectivity. A consensus-based combination of SAR backscatter change, optical water indices, and topographic constraints produced flood labels with approximately 2.6x10⁵ pixels of floods in the Mumbai Metropolitan Region during the 2019 monsoon season. A representative training set was formed using balanced stratified sampling for use in the supervised classification. Random Forest, optimized XGBoost and ensemble models were created and tested in Python using official classification measures. The tuned XGBoost model had the best performance with an overall accuracy of 71.7 percent and an area under the receiver operating characteristic curve (AUC) of 0.803, which performed better than the Random Forest and ensemble configurations. The statistical significance of the improvement in model discrimination was at the 95 percent confidence level. The analysis of ablation revealed that the model discrimination of REM and RNI increased by approximately 5–6 percent in AUC, which proves their importance in urban flood detection. There is high spatial congruency between the predicted inundation pattern and known flood-prone regions along the major drainage patterns.

The proposed framework provides a reproducible, scalable, and hydrologically informed framework for urban flood inundation mapping and has high potential for operational flood monitoring and decision support in data limited tropical cities.