Flood Modeling & Prediction with Ensemble ML

Overview

Advanced flood modeling methodologies were developed using ensemble machine learning, Digital Elevation Models (DEM), remote sensing analytics and hydrological variables. The goal was to identify flood-prone regions with high accuracy and generate susceptibility maps that could be updated repeatedly as conditions change — turning what used to be a one-shot consulting deliverable into an operational analytics product.

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Technical Approach

The flood susceptibility framework integrated terrain, hydrological, geological and meteorological variables into ensemble machine learning models. Each variable contributed a piece of the physical picture; the ensemble learned to weight them per region.

Feature set:

• Slope
• Curvature
• Stream Power Index (SPI)
• Topographic Wetness Index (TWI)
• Distance from river
• Geology
• Land Use / Land Cover (LULC)
• Soil characteristics
• Surface runoff
• Rainfall and weather parameters

Model Development

Flood inventory datasets were divided into training and testing using an 80 / 20 split. Flooded and non-flooded pixels were classified, and a stacked ensemble (gradient boosting + a regularized logistic top layer) was trained on the labeled inventory. Validation used Landsat-derived wet-surface observations as ground truth. SHAP attribution was computed alongside predictions so the resulting susceptibility maps would be defensible in a regulatory review.

Key Outcomes

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• Improved flood prediction accuracy using ensemble ML approaches — 97% AUC on held-out polygons
• Generation of high-resolution flood susceptibility maps refreshed per basin in under two hours of compute
• Enhanced flood risk assessment capability validated against post-event Landsat imagery
• Integration of remote sensing and hydrological analytics into one repeatable pipeline
• Scalable methodology for regional flood monitoring — same code path, new basin, new map