What question did this study set out to answer?

The goal is to assess urban flood vulnerability in Chhatrapati Sambhajinagar through geospatial and machine learning techniques.

February 28, 2026Discover GeoscienceOpen Access

Assessment of urban flood vulnerability using multi-criteria decision making and geospatial techniques in Chhatrapati Sambhajinagar, Maharashtra, India

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Evaluates urban flood susceptibility in an Indian city, indicating improved flood management strategies are needed.

The goal is to assess urban flood vulnerability in Chhatrapati Sambhajinagar through geospatial and machine learning techniques.
Integrated geospatial analysis with machine learning algorithms such as Random Forest, SVM, and CART.
Used eleven flood-conditioning parameters derived from remote sensing and GIS datasets.
Conducted multicollinearity analysis to ensure predictor independence.
Calibrated models using grid search-based hyperparameter tuning and tenfold cross-validation.
Random Forest model showed the highest predictive performance (Kappa = 0.887; ROC–AUC = 0.988).
Identification of areas with very high flood risk, particularly low-lying regions near the Kham River.
Western elevated zones exhibited minimal susceptibility to flooding.

Diwate et al. (2026) studied this question.

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