Deep learning forecast of rainfall-induced shallow landslides.
Where this comes from
- Record sourced from PubMed, PMID 37117189.
- Also identified by DOI 10.1038/s41467-023-38135-y and PMC identifier 10147618.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts.