{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:20:31Z","timestamp":1780435231658,"version":"3.54.1"},"reference-count":121,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prince Sattam bin Abdulaziz University","award":["PSAU\/2024\/01\/78918"],"award-info":[{"award-number":["PSAU\/2024\/01\/78918"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.<\/jats:p>","DOI":"10.3390\/rs16060988","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T03:55:34Z","timestamp":1710215734000},"page":"988","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan"],"prefix":"10.3390","volume":"16","author":[{"given":"Nafees","family":"Ali","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"China-Pakistan Joint Research Center on Earth Sciences, Islamabad 45320, Pakistan"},{"name":"Hubei Key Laboratory of Geo-Environmental Engineering, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"China-Pakistan Joint Research Center on Earth Sciences, Islamabad 45320, Pakistan"},{"name":"Hubei Key Laboratory of Geo-Environmental Engineering, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"China-Pakistan Joint Research Center on Earth Sciences, Islamabad 45320, Pakistan"},{"name":"Hubei Key Laboratory of Geo-Environmental Engineering, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashid","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Mathematical Science, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Afaq","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamza","family":"Daud","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javid","family":"Hussain","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"China-Pakistan Joint Research Center on Earth Sciences, Islamabad 45320, Pakistan"},{"name":"Hubei Key Laboratory of Geo-Environmental Engineering, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Altalbe","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"},{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s10346-021-01791-6","article-title":"Numerical Investigation of the Landslide-Debris Flow Transformation Process Considering Topographic and Entrainment Effects: A Case Study","volume":"19","author":"Guo","year":"2022","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"161430","DOI":"10.1016\/j.scitotenv.2023.161430","article-title":"Landslide Susceptibility Prediction Considering Land Use Change and Human Activity: A Case Study under Rapid Urban Expansion and Afforestation in China","volume":"866","author":"Xiong","year":"2023","journal-title":"Sci. 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