{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:31:17Z","timestamp":1771504277148,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,24]],"date-time":"2019-02-24T00:00:00Z","timestamp":1550966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC0504700"],"award-info":[{"award-number":["2017YFC0504700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The main aim of this study was to compare and evaluate the performance of fractal dimension as input data in the landslide susceptibility mapping of the Baota District, Yan\u2019an City, China. First, a total of 632 points, including 316 landslide points and 316 non-landslide points, were located in the landslide inventory map. All points were divided into two parts according to the ratio of 70%:30%, with 70% (442) of the points used as the training dataset to train the models, and the remaining, namely the validation dataset, applied for validation. Second, 13 predisposing factors, including slope aspect, slope angle, altitude, lithology, mean annual precipitation (MAP), distance to rivers, distance to faults, distance to roads, normalized differential vegetation index (NDVI), topographic wetness index (TWI), plan curvature, profile curvature, and terrain roughness index (TRI), were selected. Then, the original numerical data, box-counting dimension, and correlation dimension corresponding to each predisposing factor were calculated to generate the input data and build three classification models, namely the kernel logistic regression model (KLR), kernel logistic regression based on box-counting dimension model (KLRbox-counting), and the kernel logistic regression based on correlation dimension model (KLRcorrelation). Next, the statistical indexes and the receiver operating characteristic (ROC) curve were employed to evaluate the models\u2019 performance. Finally, the KLRcorrelation model had the highest area under the curve (AUC) values of 0.8984 and 0.9224, obtained by the training and validation datasets, respectively, indicating that the fractal dimension can be used as the input data for landslide susceptibility mapping with a better effect.<\/jats:p>","DOI":"10.3390\/e21020218","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T03:06:52Z","timestamp":1551064012000},"page":"218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Assessment of Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with Kernel Logistic Regression Model"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5589-3318","authenticated-orcid":false,"given":"Tingyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Earth Science and Resources, Chang\u2019an University, Key Laboratory of Degraded and Unutilized Land Remediation Engineering, Ministry of Land and Resources, Shaanxi Provincial Key Laboratory of Land Rehabilitation, Xi\u2019an 710064, China"}]},{"given":"Ling","family":"Han","sequence":"additional","affiliation":[{"name":"School of Earth Science and Resources, Chang\u2019an University, Key Laboratory of Degraded and Unutilized Land Remediation Engineering, Ministry of Land and Resources, Shaanxi Provincial Key Laboratory of Land Rehabilitation, Xi\u2019an 710064, China"}]},{"given":"Jichang","family":"Han","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Land Engineering Construction Group Co. Ltd., Xi\u2019an 710075, China"},{"name":"School of Geological and Surveying &amp; Mapping Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Xian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geological and Surveying &amp; Mapping Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Science and Resources, Chang\u2019an University, Key Laboratory of Degraded and Unutilized Land Remediation Engineering, Ministry of Land and Resources, Shaanxi Provincial Key Laboratory of Land Rehabilitation, Xi\u2019an 710064, China"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Land Engineering Construction Group Co. Ltd., Xi\u2019an 710075, China"},{"name":"School of Geological and Surveying &amp; Mapping Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1038\/srep09899","article-title":"Landslide susceptibility mapping using gis-based statistical models and remote sensing data in tropical environment","volume":"5","author":"Shahabi","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.scitotenv.2018.01.124","article-title":"Landslide susceptibility modelling using gis-based machine learning techniques for chongren county, jiangxi province, China","volume":"626","author":"Chen","year":"2018","journal-title":"Sci. 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