{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T16:14:47Z","timestamp":1773072887397,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region","award":["grant number 2018D04027"],"award-info":[{"award-number":["grant number 2018D04027"]}]},{"name":"the Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region","award":["grant number 2021-XBQNXZ-012"],"award-info":[{"award-number":["grant number 2021-XBQNXZ-012"]}]},{"name":"the Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region","award":["grant number 2022B03001-3"],"award-info":[{"award-number":["grant number 2022B03001-3"]}]},{"name":"\u201cWestern Light\u201d Talents Training Program of CAS","award":["grant number 2018D04027"],"award-info":[{"award-number":["grant number 2018D04027"]}]},{"name":"\u201cWestern Light\u201d Talents Training Program of CAS","award":["grant number 2021-XBQNXZ-012"],"award-info":[{"award-number":["grant number 2021-XBQNXZ-012"]}]},{"name":"\u201cWestern Light\u201d Talents Training Program of CAS","award":["grant number 2022B03001-3"],"award-info":[{"award-number":["grant number 2022B03001-3"]}]},{"name":"the Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["grant number 2018D04027"],"award-info":[{"award-number":["grant number 2018D04027"]}]},{"name":"the Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["grant number 2021-XBQNXZ-012"],"award-info":[{"award-number":["grant number 2021-XBQNXZ-012"]}]},{"name":"the Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["grant number 2022B03001-3"],"award-info":[{"award-number":["grant number 2022B03001-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide disasters frequently occur along the highway G30 in the Guozigou Valley, the corridor of energy, material, economic and cultural exchange, etc., between Yili and other cities of China and Central Asia. However, little attention has been paid to assess the detailed landslide susceptibility of the strategically important highway, especially with high spatial resolution data and the generative presence-only MaxEnt model. Landslide susceptibility assessment (LSA) is a first and vital step for preventing and mitigating landslide hazards. The goal of the current study was to perform LSA for the landslide-prone highway G30 in Guozigou Valley, China with the aid of GIS tools and Chinese high resolution Gaofen-1 (GF-1) satellite data, and analyze and compare the performance of the maximum entropy (MaxEnt) model and logistic regression (LR). Thirty five landslides were determined in the study region, using GF-1 satellite data, official data, and field surveys. Seven landslide conditioning factors, including altitude, slope, aspect, gully density, lithology, faults density, and NDVI, were used to investigate their existing spatial relationships with landslide occurrences. The LR and MaxEnt model performance were assessed by the receiver operating characteristic curve, presenting areas under the curve equal to 0.85 and 0.94, respectively. The performance of the MaxEnt model was slightly better than that of the LR model. A landslide susceptibility map was created through reclassifying the landslides occurrence probability with the classification method of natural breaks. According to the MaxEnt model results, 3.29% and 3.82% of the study region is highly and very highly susceptible to future landslide events, respectively, with the highest landslide susceptibility along the highway. The generated landslide susceptibility map could help government agencies and decision-makers to make wise decisions for preventing or mitigating landslide hazards along the highway and design schemes of highway engineering and maintenance in Guozigou Valley, the mountainous areas.<\/jats:p>","DOI":"10.3390\/rs14153620","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T22:43:26Z","timestamp":1659048206000},"page":"3620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Chinese High Resolution Satellite Data and GIS-Based Assessment of Landslide Susceptibility along Highway G30 in Guozigou Valley Using Logistic Regression and MaxEnt Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Ying","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Liangjun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644000, China"}]},{"given":"Anming","family":"Bao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1054-5966","authenticated-orcid":false,"given":"Junli","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaobing","family":"Yan","sequence":"additional","affiliation":[{"name":"Transport Department of Xinjiang Uygur Autonomous Region, Urumqi 830000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.geomorph.2012.08.004","article-title":"Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models","volume":"179","author":"Das","year":"2012","journal-title":"Geomorphology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1007\/s11069-016-2591-7","article-title":"Landslide susceptibility mapping of the Sera River basin using logistic regression model","volume":"85","author":"Raja","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.enggeo.2011.09.011","article-title":"Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS","volume":"124","author":"Choi","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1515\/geo-2019-0017","article-title":"Selected components of geological structures and numerical modelling of slope stability","volume":"11","author":"Kaczmarek","year":"2019","journal-title":"Open Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0013-7952(01)00093-X","article-title":"Landslide risk assessment and management: An overview","volume":"64","author":"Dai","year":"2002","journal-title":"Eng. Geol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/j.geomorph.2009.09.023","article-title":"Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)","volume":"114","author":"Das","year":"2010","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.geomorph.2011.03.001","article-title":"Integrating physical and empirical landslide susceptibility models using generalized additive models","volume":"129","author":"Goetz","year":"2011","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s10584-015-1357-7","article-title":"Adapting transportation to climate change on federal lands in Washington State, U.S.A","volume":"130","author":"Strauch","year":"2015","journal-title":"Clim. Chang."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10064-005-0023-0","article-title":"Landslide hazard and risk zonation\u2014Why is it still so difficult?","volume":"65","author":"Soeters","year":"2006","journal-title":"Bull. Eng. Geol. Env."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/14615517.2016.1176403","article-title":"Spatial analysis of environmental impacts of highway projects with special emphasis on mountainous area: An overview","volume":"34","author":"Banerjee","year":"2016","journal-title":"Impact Assess. Proj. Apprais."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.enggeo.2008.03.018","article-title":"A review of assessing landslide frequency for hazard zoning purposes","volume":"102","author":"Corominas","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geomorph.2004.06.010","article-title":"The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan","volume":"65","author":"Ayalew","year":"2005","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8647","DOI":"10.1007\/s12665-015-4028-0","article-title":"Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran","volume":"73","author":"Shahabi","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geomorph.2015.06.001","article-title":"Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)","volume":"249","author":"Trigila","year":"2015","journal-title":"Geomorphology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.gsf.2020.05.010","article-title":"Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia","volume":"12","author":"Youssef","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2007.01.003","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations","volume":"72","author":"Yalcin","year":"2008","journal-title":"Catena"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s12303-014-0032-8","article-title":"Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models","volume":"19","author":"Youssef","year":"2015","journal-title":"Geosci. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0013-7952(03)00142-X","article-title":"Determination and application of the weights for landslide susceptibility mapping using an artificial neural network","volume":"71","author":"Lee","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Tuan","year":"2016","journal-title":"Landslides"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.catena.2018.03.003","article-title":"Review on landslide susceptibility mapping using support vector machines","volume":"165","author":"Huang","year":"2018","journal-title":"Catena"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1016\/j.scitotenv.2018.06.389","article-title":"Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and na\u00efve Bayes tree for landslide susceptibility modeling","volume":"644","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1002\/jgrf.20099","article-title":"Detecting fingerprints of landslide drivers: A MaxEnt model","volume":"118","author":"Convertino","year":"2013","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s12665-014-3442-z","article-title":"Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets","volume":"73","author":"Park","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.catena.2017.01.010","article-title":"Landslide susceptibility assessment using maximum entropy model with two different data sampling methods","volume":"152","author":"Kornejady","year":"2017","journal-title":"Catena"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.gsf.2020.06.013","article-title":"Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran","volume":"12","author":"Ngo","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104805","DOI":"10.1016\/j.catena.2020.104805","article-title":"Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping","volume":"195","author":"Pham","year":"2020","journal-title":"Catena"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101211","DOI":"10.1016\/j.gsf.2021.101211","article-title":"Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization","volume":"12","author":"Zhou","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geoderma.2017.06.020","article-title":"Landslide spatial modelling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques","volume":"305","author":"Chen","year":"2017","journal-title":"Geoderma"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.catena.2015.07.020","article-title":"A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran","volume":"135","author":"Dehnavi","year":"2015","journal-title":"Catena"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.catena.2018.07.012","article-title":"Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping","volume":"171","author":"Zhu","year":"2018","journal-title":"Catena"},{"key":"ref_33","first-page":"175","article-title":"Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study","volume":"10","author":"Cuartero","year":"2012","journal-title":"Landslides"},{"key":"ref_34","first-page":"693","article-title":"Evaluation on geological hazard risk and disaster-causing factors in the Guozigou Valley in Ili, Xinjiang","volume":"34","author":"Zhao","year":"2017","journal-title":"Arid. Zone Res."},{"key":"ref_35","first-page":"203","article-title":"A method to assess landslide susceptibility by using logistic regression model for Guozigou Region, Xinjinag","volume":"32","author":"Zhao","year":"2017","journal-title":"Mt. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multiscale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s10346-019-01136-4","article-title":"Rapid prediction of the magnitude scale of landslide events triggered by an earthquake","volume":"16","author":"Tanyas","year":"2019","journal-title":"Landslides"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0169-555X(01)00087-3","article-title":"Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong","volume":"42","author":"Dai","year":"2002","journal-title":"Geomorphology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11069-012-0347-6","article-title":"Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya","volume":"65","author":"Devkota","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s12517-015-2094-y","article-title":"GIS-based landslide spatial modeling in Ganzhou City, China","volume":"9","author":"Hong","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.geomorph.2018.09.019","article-title":"New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China","volume":"324","author":"Yang","year":"2019","journal-title":"Geomorphology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.geomorph.2009.06.006","article-title":"Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)","volume":"113","author":"Kawabata","year":"2009","journal-title":"Geomorphology"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sidle, R.C., and Ochiai, H. (2006). Landslides: Processes, Prediction, and Land Use, American Geophysical Union.","DOI":"10.1029\/WM018"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s002540100310","article-title":"Statistical analysis of landslide susceptibility at Yongin, Korea","volume":"40","author":"Lee","year":"2001","journal-title":"Environ. Geol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1002\/esp.1562","article-title":"GIS-based regional landslide susceptibility mapping: A case study in southern California","volume":"33","author":"He","year":"2008","journal-title":"Earth Surf. Proc. Land."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.geomorph.2005.12.003","article-title":"Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)","volume":"76","author":"Vanwalleghem","year":"2006","journal-title":"Geomorphology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"853","DOI":"10.5194\/nhess-5-853-2005","article-title":"Spatial prediction models for landslide hazards: Review, comparison and evaluation","volume":"5","author":"Brenning","year":"2005","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.ecolmodel.2005.03.026","article-title":"Maximum entropy modeling of species geographic distributions","volume":"190","author":"Phillips","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_50","unstructured":"Phillips, S.J., Dud\u00edk, M., and Schapire, R.E. (2021, July 25). Maxent Software for Modeling Species Niches and Distributions (Version 3.4.1). Available online: http:\/\/biodiversityinformatics.amnh.org\/open_source\/maxent\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11069-005-5182-6","article-title":"Validation and evaluation of predictive models in hazard and risk assessment","volume":"37","year":"2006","journal-title":"Nat. Hazards"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.catena.2019.104425","article-title":"A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China","volume":"188","author":"Wang","year":"2020","journal-title":"Catena"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1130\/0091-7613(1975)3<393:IOCARC>2.0.CO;2","article-title":"Impact of clear-cutting and road construction on soil erosion by landslides in the western Cascade Range, Oregon","volume":"3","author":"Swanson","year":"1975","journal-title":"Geology"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s11069-010-9614-6","article-title":"Unprecedented rates of landslide and surface erosion along a newly constructed road in Yunnan, China","volume":"57","author":"Sidle","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.geomorph.2011.10.029","article-title":"Geomorphic process rates of landslides along a humidity gradient in the tropical Andes","volume":"139","author":"Muenchow","year":"2012","journal-title":"Geomorphology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/nhess-15-45-2015","article-title":"Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province","volume":"15","author":"Brenning","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s00477-019-01696-w","article-title":"Spatial prediction of landslide susceptibility in Taleghan basin, Iran","volume":"33","author":"Mokhtari","year":"2019","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"104364","DOI":"10.1016\/j.catena.2019.104364","article-title":"Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping","volume":"187","author":"Pourghasemi","year":"2020","journal-title":"Catena"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.catena.2012.05.005","article-title":"Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran","volume":"97","author":"Pourghasemi","year":"2012","journal-title":"Catena"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s11069-018-3356-2","article-title":"Landslide susceptibility assessment by Dempster\u2013Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran","volume":"93","author":"Shirani","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"104221","DOI":"10.1016\/j.catena.2019.104221","article-title":"Performance evaluation for four Gis-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China","volume":"183","author":"Jiao","year":"2019","journal-title":"Catena"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10064-017-1055-y","article-title":"An ensemble landslide hazard model incorporating rainfall threshold for Mt. Umyeon, South Korea","volume":"78","author":"Pradhan","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1007\/s11069-019-03617-0","article-title":"Application of the coupled TOPSIS\u2013Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran","volume":"96","author":"Sheikh","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6496","DOI":"10.1038\/s41598-021-85862-7","article-title":"Evaluation of multi-hazard map produced using MaxEnt machine learning technique","volume":"11","author":"Javidan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1007\/s10064-019-01687-9","article-title":"The dilemma of determining the superiority of data mining models: Optimal sampling balance and end users\u2019 perspectives matter","volume":"79","author":"Teimouri","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s12665-018-7762-2","article-title":"An integrated data-mining and multi-criteria decision-making approach for hazard-based object ranking with a focus on landslides and floods","volume":"77","author":"Mirzaei","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1890\/07-2153.1","article-title":"Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data","volume":"19","author":"Phillips","year":"2009","journal-title":"Ecol. 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