{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T05:44:27Z","timestamp":1762062267599,"version":"build-2065373602"},"publisher-location":"Basel Switzerland","reference-count":3,"publisher":"MDPI","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.3390\/environsciproc2022017038","type":"proceedings-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T21:07:26Z","timestamp":1660165646000},"page":"38","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Performance and Efficiency of Machine Learning Based Approaches for Wildfire Susceptibility Mapping"],"prefix":"10.3390","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3592-8920","authenticated-orcid":false,"given":"Marj","family":"Tonini","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, University of Lausanne, 1015 Lausanne, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6603-7453","authenticated-orcid":false,"given":"Mario G.","family":"Pereira","sequence":"additional","affiliation":[{"name":"Centro de Investiga\u00e7\u00e3o e de Tecnologias Agro-Ambientais e Biol\u00f3gicas (CITAB), Universidade de Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"given":"Paolo","family":"Fiorucci","sequence":"additional","affiliation":[{"name":"CIMA Research Foundation, 17100 Savona, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.envsoft.2017.12.019","article-title":"Wildfire susceptibility mapping: Deterministic vs. stochastic approaches","volume":"101","author":"Leuenberger","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tonini, M., D\u2019andrea, M., Biondi, G., Esposti, S., Trucchia, A., and Fiorucci, P. (2020). A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy. Geosciences, 10.","DOI":"10.20944\/preprints202001.0385.v1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bustillo S\u00e1nchez, M., Tonini, M., Mapelli, A., and Fiorucci, P. (2021). Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia) Using Random Forest. Geosciences, 11.","DOI":"10.3390\/geosciences11050224"}],"event":{"name":"International Conference on Fire Behavior and Risk","acronym":"ICFBR 2022"},"container-title":["The Third International Conference on Fire Behavior and Risk"],"original-title":[],"link":[{"URL":"https:\/\/www.mdpi.com\/2673-4931\/17\/1\/38\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:06:30Z","timestamp":1760141190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-4931\/17\/1\/38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,9]]},"references-count":3,"alternative-id":["environsciproc2022017038"],"URL":"https:\/\/doi.org\/10.3390\/environsciproc2022017038","relation":{},"subject":[],"published":{"date-parts":[[2022,8,9]]}}}