High-Resolution Flood Exposure Mapping for Pakistan
A comprehensive national-scale assessment identifying flood exposure for ~95 million people using machine learning and high-resolution geospatial data
The Challenge
Pakistan faces severe flood risks due to monsoons, glacial lake outburst floods (GLOFs), and changing climate patterns. The devastating 2022 floods affected 33 million people and caused $30+ billion in damages.
Traditional flood mapping approaches were either too coarse for actionable decision-making or too expensive to scale nationally. Government agencies needed:
- High-resolution, nationally consistent flood susceptibility maps
- Population exposure assessment for disaster preparedness
- Scientifically validated methodologies for policy decisions
- Cost-effective approaches using available data
Our Approach
We developed a comprehensive machine learning framework using Google Earth Engine and high-performance computing to create the first high-resolution national flood susceptibility map of Pakistan.
Key Innovation: Multi-Algorithm Ensemble
We evaluated 14 different machine learning algorithms and selected the optimal ensemble approach, achieving 95% AUC accuracy while maintaining computational efficiency for national-scale analysis.
Scientific Methodology
Data Integration
Integrated 15+ geospatial variables including topography, hydrology, climate, soil properties, and land cover using Google Earth Engine's planetary-scale data catalog.
ML Optimization
Tested 14 algorithms (Random Forest, XGBoost, LGBM, SVM, etc.) with systematic hyperparameter tuning and 10-fold cross-validation to identify optimal approach.
Validation & Analysis
Rigorous validation using historical flood records, ROC analysis achieving 95% AUC, and SHAP analysis for explainable AI interpretation.
Technical Specifications
Data Sources
- • SRTM-GL1 30m Digital Elevation Model
- • Landsat 8-9 Surface Reflectance
- • CHIRPS Precipitation Data
- • OpenStreetMap Hydrography
- • MODIS Land Cover (MCD12Q1)
- • SoilGrids Soil Properties
Processing Details
- • Google Earth Engine Cloud Computing
- • 30m × 30m pixel resolution
- • ~22 million training samples
- • Stratified random sampling
- • 10-fold cross-validation
- • SHAP explainability analysis
Key Findings & Impact
Massive Exposure Identified
~95 million people (47% of Pakistan's population) live in areas classified as high flood susceptibility, with Punjab and Sindh provinces most affected.
Validated Accuracy
Model achieved 95% AUC accuracy when validated against historical flood events, providing confidence for operational deployment.
Explainable Results
SHAP analysis revealed elevation, distance to rivers, and precipitation as key drivers, enabling targeted intervention strategies.
Real-World Impact
This research provides Pakistan's government with the first scientifically validated, high-resolution national flood exposure assessment, enabling evidence-based disaster risk reduction policies and resource allocation for the world's 5th most populous country.
Published Research
A high-resolution national-scale flood susceptibility and exposure assessment for Pakistan
Authors: Waleed, M., Sajjad, M., Shazil, M.S., et al.
Journal: International Journal of Disaster Risk Reduction (2024)
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