{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:09:27Z","timestamp":1774613367351,"version":"3.50.1"},"reference-count":130,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["42177155"],"award-info":[{"award-number":["42177155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["2017JQ4008"],"award-info":[{"award-number":["2017JQ4008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program of Shaanxi Province, China","award":["42177155"],"award-info":[{"award-number":["42177155"]}]},{"name":"Natural Science Basic Research Program of Shaanxi Province, China","award":["2017JQ4008"],"award-info":[{"award-number":["2017JQ4008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70\/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features.<\/jats:p>","DOI":"10.3390\/rs15204952","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T10:04:39Z","timestamp":1697191479000},"page":"4952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5292-3969","authenticated-orcid":false,"given":"Hui","family":"Shang","sequence":"first","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Lixiang","family":"Su","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7396-4754","authenticated-orcid":false,"given":"Paraskevas","family":"Tsangaratos","sequence":"additional","affiliation":[{"name":"Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4436-4784","authenticated-orcid":false,"given":"Ioanna","family":"Ilia","sequence":"additional","affiliation":[{"name":"Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece"}]},{"given":"Sihang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Shaobo","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Zhao","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.catena.2015.05.019","article-title":"Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines","volume":"133","author":"Hong","year":"2015","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10064-018-1256-z","article-title":"Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)","volume":"78","author":"Chen","year":"2018","journal-title":"Bull. 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