AI-Driven Flood Prediction in Urban Areas using Sentinel-1 SAR and Machine Learning
Abstract
Development of real-time flood prediction system combining Sentinel-1 SAR data with XGBoost and LSTM models, achieving 94.3% accuracy in predicting flood-prone areas 48 hours in advance.
Citation
Dr. Kwame Osei, Amina Hassan (2024). AI-Driven Flood Prediction in Urban Areas using Sentinel-1 SAR and Machine Learning. Remote Sensing of Environment.
Dr. Kwame Osei, Amina Hassan. "AI-Driven Flood Prediction in Urban Areas using Sentinel-1 SAR and Machine Learning." Remote Sensing of Environment, 2024.
Dr. Kwame Osei, Amina Hassan. "AI-Driven Flood Prediction in Urban Areas using Sentinel-1 SAR and Machine Learning." Remote Sensing of Environment (2024).
@article{spationex,
title={AI-Driven Flood Prediction in Urban Areas using Sentinel-1 SAR and Machine Learning},
author={Dr. Kwame Osei, Amina Hassan},
journal={Remote Sensing of Environment},
year={2024},
volume={301},
pages={113-125},
doi={null}
}Urban flooding poses significant risks to infrastructure, economies, and human lives. This research presents an AI-driven framework for early flood prediction in Nairobi's informal settlements using Synthetic Aperture Radar (SAR) data from Sentinel-1 satellite.
Methodology
We combined Sentinel-1 C-band SAR data (VV and VH polarizations) with meteorological data from local stations. The framework employs:
1. XGBoost for feature importance analysis and initial classification
2. LSTM Networks for temporal sequence prediction
3. Random Forest for ensemble learning and uncertainty quantification
Results
The system achieved:
• 94.3% accuracy in predicting flood-prone areas 48 hours in advance
• F1-Score of 0.91 for binary classification of flood/no-flood scenarios
• Reduction of false alarms by 37% compared to traditional hydrological models
The model successfully predicted the April 2023 floods in Mathare Valley with 96% accuracy, enabling early evacuation of 2,300 households.
Publication Details
Keywords
Share This Research
Continue Exploring
Discover more research in geospatial intelligence and climate resilience.