NelsonUzoma/Using-Machine-Learning-to-Improve-Flood-Prediction-in-Coastal-West-Africa

Machine learning research exploring flood prediction in coastal West Africa using environmental and meteorological data. # Flood Prediction in Coastal West Africa ## Overview Flooding is a major environmental and humanitarian challenge in many coastal regions of West Africa. Rapid urbanization, climate change, and limited infrastructure have increased the frequency and severity of flood events in cities such as Lagos, Accra, and Cotonou. This project explores how machine learning methods could improve flood prediction by combining multiple data sources including rainfall measurements, river discharge data, satellite imagery, and topographical information. The goal is to investigate how computational approaches can contribute to early warning systems and disaster preparedness. ## Repository Contents Flood_Prediction_West_Africa.pdf The full research paper outlining the motivation, proposed methods, and expected outcomes of the study. code/flood_prediction_model.py Example Python script demonstrating how environmental variables might be used to train a machine learning model for flood prediction. data/sample_dataset_description.txt Description of the types of environmental datasets that would be required for this research. ## Motivation Flood events in coastal West Africa often cause severe economic damage and displacement of communities. Traditional forecasting approaches rely on limited hydrological monitoring infrastructure. Machine learning offers the ability to integrate multiple environmental signals and detect patterns that may not be visible through conventional models. ## Future Work Future work could involve collecting real environmental datasets, implementing predictive models such as Random Forest or Gradient Boosting, and evaluating model performance using historical flood events. ## Author Independent computer science research project exploring applications of machine learning for environmental risk prediction.