RISE Research Radar

Computer Science Open House 2022-2025

2022

Machine learning for predicting water flow intensity based on physical characteristics of catchment area

Aleksis Pirinen, Olof Mogren

Summary

Data-driven AI model for predicting water flow intensity using spatial maps and temporal inputs (rainfall, temperature) to address flooding risks from climate change. Uses U-Net architecture. Pre-study for better predictive models.

Themes

machine-learningsustainability

Keywords

water flow prediction, flooding, climate change, U-Net, spatial modeling

Poster

Machine learning for predicting water flow intensity based on physical characteristics of catchment area poster

Click image to open full size