Flood Risk Prediction in the Nile Basin
A machine learning-based early warning system designed to predict extreme flood risk events in the Nile Basin, with a focus on Egypt, using high-resolution climate datasets and advanced machine learning models.
Climate change is increasing the frequency and intensity of extreme weather events such as heavy rainfall and floods. Predicting these events early can significantly help governments and infrastructure planners mitigate risks.
This project develops a Machine Learning classification system capable of predicting flood risk levels based on climate variables including precipitation and temperature.
The system analyzes historical and projected climate data under different climate change scenarios and classifies flood risk into:
Low Risk
Medium Risk
High Risk
The final model will be deployed through an interactive web application that allows users to input climate conditions and receive real-time flood risk predictions.
Data Source
The project uses CORDEX Climate Data provided by the Nile Basin Initiative (NBI).
Dataset includes:
Daily precipitation (pr)
Maximum temperature (tasmax)
Minimum temperature (tasmin)
Climate projections are analyzed under the following scenarios:
RCP 4.5 – Moderate climate change scenario
RCP 8.5 – High emission scenario
These datasets allow the model to study the potential impact of future climate conditions on flood risks.
