{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:58:21Z","timestamp":1780462701756,"version":"3.54.1"},"reference-count":73,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019890","name":"Centre for Advanced Modelling and Geospatial lnformation Systems, University of Technology Sydney","doi-asserted-by":"publisher","award":["Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"],"award-info":[{"award-number":["Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"]}],"id":[{"id":"10.13039\/501100019890","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["Researchers Supporting Project number RSP-2021\/14"],"award-info":[{"award-number":["Researchers Supporting Project number RSP-2021\/14"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).<\/jats:p>","DOI":"10.3390\/rs13163281","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T09:58:06Z","timestamp":1629367086000},"page":"3281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation"],"prefix":"10.3390","volume":"13","author":[{"given":"Husam A. H.","family":"Al-Najjar","sequence":"first","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology, Sydney, NSW 2007, Australia"},{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-3463","authenticated-orcid":false,"given":"Bahareh","family":"Kalantar","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maher Ibrahim","family":"Sameen","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Santosh","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences Beijing, Beijing 100083, China"},{"name":"Department of Earth Sciences, University of Adelaide, Adelaide, SA 5005, Australia"},{"name":"State Key Laboratory of Continental Dynamics, Department of Geology, Northwest University, Xi\u2019an 710069, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/978-3-319-77377-3_13","article-title":"Machine learning techniques in landslide susceptibility mapping: A survey and a case study","volume":"50","author":"Kavzoglu","year":"2018","journal-title":"Landslides Theory Pract. 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