Machine Learning • Hydrology • LSTM
Machine Learning for River Forecasting
Leveraging LSTM neural networks to predict river stage levels at Avon and Ballantyne
stream gages for up to 48 hours ahead, enhancing flood risk assessment.
This project leverages Machine Learning models to predict river stage levels for up to 48 hours ahead.
The goal is to enhance flood risk assessment using real-time data and AI techniques.
Duration: 2022 - 2023 | Location: Genesee River Basin
- Forecast river stages in the Genesee River Basin
- Improve peak flow prediction for flood management
- Enable real-time forecasting with dynamic inputs
- Develop scalable models for other river systems
- Flow & stage time series from HEC-DSS files
- USGS streamgage data (Avon & Ballantyne)
- Precipitation forecasts from NOAA
- Historical flood event data
LSTM
Primary Model
48h
Forecast Horizon
2+
Gauge Stations
- Long Short-Term Memory (LSTM) neural networks
- Random Forest comparison approach
- Feature engineering with lagged variables
- Validation using historical flood events
Programming & Libraries
- Python – Core ML development
- TensorFlow & Keras – Deep learning frameworks
- NumPy & Pandas – Data preprocessing
- Scikit-learn – Model evaluation
Visualization
- Matplotlib & Seaborn – Data visualization
- Plotly – Interactive dashboards
- HEC-DSSVue – Time-series analysis
