{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:30:08Z","timestamp":1776843008914,"version":"3.51.2"},"reference-count":86,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T00:00:00Z","timestamp":1636502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007511","name":"King Juan Carlos University","doi-asserted-by":"publisher","award":["PFIRMA-c833-d379-84a0-03ab-757c-d7fd-d2cf-4d9b"],"award-info":[{"award-number":["PFIRMA-c833-d379-84a0-03ab-757c-d7fd-d2cf-4d9b"]}],"id":[{"id":"10.13039\/501100007511","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007511","name":"King Juan Carlos University","doi-asserted-by":"publisher","award":["PREDOC21-029"],"award-info":[{"award-number":["PREDOC21-029"]}],"id":[{"id":"10.13039\/501100007511","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["PRE2019-089208"],"award-info":[{"award-number":["PRE2019-089208"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslides are recognized as high-impact natural hazards in different regions around the world; therefore, they are extensively researched by experts. Landslide inventories are essential to identify areas that are likely to be affected in the future, thereby enabling interventions to prevent loss of life. Today, through combined approaches, such as remote sensing and machine learning techniques, it is possible to apply algorithms that use data derived from satellite images to produce landslide inventories. This work presents the performance of five machine learning methods\u2014k-nearest neighbor (KNN), stochastic gradient descendent (SGD), support vector machine radial basis function (SVM RBF Kernel), support vector machine (SVM linear kernel), and AdaBoost\u2014in landslide detection in a zone of the state of Guerrero in southern Mexico, using continuous change maps and primary landslide factors, such as slope angle, terrain orientation (aspect), and lithology, as inputs. The models were trained with 2\/3 of ground truth samples of 671 slidden\/non-slidden polygons. The obtained inventory maps were evaluated with the remaining 1\/3 of ground truth samples by generating a confusion matrix and applying the Kappa concordance coefficient, accuracy, precision, recall, and F1 score as evaluation metrics, as well as omission and commission errors. According to the results, the AdaBoost classifier reached greater spatial and statistical coherence than the other implemented methods. The best input layer combination for detection was the continuous change maps obtained by the linear regression and image differencing detection methods, together with the slope angle, aspect, and lithology conditioning factors.<\/jats:p>","DOI":"10.3390\/rs13224515","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:04:46Z","timestamp":1636671886000},"page":"4515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6284-3263","authenticated-orcid":false,"given":"Roc\u00edo N.","family":"Ramos-Bernal","sequence":"first","affiliation":[{"name":"Cuerpo Acad\u00e9mico UAGro CA-93 Riesgos Naturales y Geotecnolog\u00eda, FI, Universidad Aut\u00f3noma de Guerrero, Av. L\u00e1zaro C\u00e1rdenas s\/n, CU, Chilpancingo 39070, GE, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1693-8303","authenticated-orcid":false,"given":"Ren\u00e9","family":"V\u00e1zquez-Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Cuerpo Acad\u00e9mico UAGro CA-93 Riesgos Naturales y Geotecnolog\u00eda, FI, Universidad Aut\u00f3noma de Guerrero, Av. L\u00e1zaro C\u00e1rdenas s\/n, CU, Chilpancingo 39070, GE, Mexico"},{"name":"Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6423-3490","authenticated-orcid":false,"given":"Claudia A.","family":"Cant\u00fa-Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Ingenier\u00eda para la Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico, Universidad Aut\u00f3noma de Guerrero, Av\/L\u00e1zaro C\u00e1rdenas s\/n, CU, Chilpancingo 39070, GE, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9785-1252","authenticated-orcid":false,"given":"Antonio","family":"Alarc\u00f3n-Paredes","sequence":"additional","affiliation":[{"name":"Cuerpo Acad\u00e9mico UAGro CA-178 Desarrollo Tecnol\u00f3gico Aplicado, Universidad Aut\u00f3noma de Guerrero, Av\/L\u00e1zaro C\u00e1rdenas s\/n, CU, Chilpancingo 39070, GE, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2699-140X","authenticated-orcid":false,"given":"Gustavo A.","family":"Alonso-Silverio","sequence":"additional","affiliation":[{"name":"Cuerpo Acad\u00e9mico UAGro CA-178 Desarrollo Tecnol\u00f3gico Aplicado, Universidad Aut\u00f3noma de Guerrero, Av\/L\u00e1zaro C\u00e1rdenas s\/n, CU, Chilpancingo 39070, GE, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5364-6388","authenticated-orcid":false,"given":"Adri\u00e1n","family":"G. Bruz\u00f3n","sequence":"additional","affiliation":[{"name":"Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1724-6173","authenticated-orcid":false,"given":"F\u00e1tima","family":"Arrogante-Funes","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Teledetecci\u00f3n Ambiental, Unidad Docente de Geograf\u00eda, Geolog\u00eda y Medio Ambiente, \u00c1rea de Geograf\u00eda, Universidad de Alcal\u00e1, Filosof\u00eda y Letras, C\/Colegios 2, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fidel","family":"Mart\u00edn-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"\u00c1rea de Geolog\u00eda, ESCET, Universidad Rey Juan Carlos, c\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3501-7051","authenticated-orcid":false,"given":"Carlos J.","family":"Novillo","sequence":"additional","affiliation":[{"name":"Research Group on Technologies for Landscape Analysis and Diagnosis (TADAT), Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1944-777X","authenticated-orcid":false,"given":"Patricia","family":"Arrogante-Funes","sequence":"additional","affiliation":[{"name":"Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"ref_1","unstructured":"CRED-UNISDR (2021, January 07). 2015 Disasters in Numbers. 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