Project Brief: Flood Prediction Algorithm for Emilia-Romagna

Objective: Develop a machine learning algorithm to predict flood events in the Emilia-Romagna region of Italy, enhancing early warning systems and improving disaster preparedness.

Key Components:

1. Data Collection:

   • Historical flood data for Emilia-Romagna
   • Meteorological data (rainfall, temperature, humidity)
   • Hydrological data (river levels, soil moisture)
   • Topographical data (elevation, land use)
   • Hydrometric level of rivers
   • Satellite imagery

2. Data Preprocessing:

   • Clean and normalize data
   • Handle missing values
   • Feature engineering

3. Algorithm Development:

   • Explore various ML models (e.g., Random Forests, Neural Networks, LSTM)
   • Train models on historical data
   • Implement time-series analysis for temporal patterns

4. Model Evaluation:

   • Use cross-validation techniques
   • Evaluate using metrics like precision, recall, and F1-score
   • Compare performance against existing flood prediction methods

5. Integration with GIS:

   • Incorporate geographical information systems for spatial analysis
   • Develop flood risk maps

6. Real-time Data Integration:

   • Design system to incorporate real-time weather and hydrological data

7. User Interface:

   • Create a dashboard for visualizing predictions and risk levels

8. Validation and Testing:

   • Conduct thorough testing with recent flood events
   • Collaborate with local authorities for real-world validation

Expected Outcomes:

• Accurate flood prediction model for Emilia-Romagna
• Improved lead time for flood warnings
• Enhanced decision-making tools for emergency management

Useful links

https://allertameteo.regione.emilia-romagna.it/livello-idrometrico https://github.com/amgrg/Caravan.git https://confluence.ecmwf.int/display/CEMS/EFAS+User+Guide