Landslide susceptibility maps using ensemble machine learning models on basin and regional level in Lombardy, Italy

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