Landslide susceptibility maps using base machine learning models on basin and regional level in Lombardy, Italy
- 1. Department of Civil and Environmental Engineering, Politecnico di Milano
Description
A selection of landslide susceptibility maps computed through base machine learning models for the basins of Val Tartano, Upper Valtellina and Valchiavenna, and on a regional level for the Lombardy region in Italy.
A list of the used machine learning methods:
- Bagging,
- Random Forest,
- AdaBoost,
- Gradient Tree Boosting,
- Neural Networks.
A full list of the model combinations can be found in the "Case Studies" document.
The maps are in WGS 84/ UTM zone 32N (EPSG:32632).
The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:
Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case
study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263
The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.
The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.