Machine learning models for river flow forecasting in small catchments.

ABSTRACT: In consideration of ongoing climate changes, it has been necessary to provide new tools capable of mitigating hydrogeological risks. These effects will be more marked in small catchments, where the geological and environmental contexts do not require long warning times to implement risk mitigation measures. In this context, deep learning models can be an effective tool for local authorities to have solid forecasts of outflows and to make correct choices during the alarm phase. In this study, we investigate the use of deep learning models able to forecast hydrometric height in very fast hydrographic basins. The errors of the models are very small and about a few centimetres, with several forecasting hours. The models allow a prediction of extreme events with also 4-6 h (RMSE of about 10-30 

SUBMITTER: Luppichini M 

PROVIDER: S-EPMC11538420 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature