{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:24:58Z","timestamp":1775071498918,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T00:00:00Z","timestamp":1554422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41202197"],"award-info":[{"award-number":["41202197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the key Projects of the National Natural Science Foundation of China","award":["41330636"],"award-info":[{"award-number":["41330636"]}]},{"name":"Jilin Provincial Science and Technology Department","award":["20170101001JC"],"award-info":[{"award-number":["20170101001JC"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["20100471265 and 2017M621212"],"award-info":[{"award-number":["20100471265 and 2017M621212"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.<\/jats:p>","DOI":"10.3390\/e21040372","type":"journal-article","created":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T11:36:01Z","timestamp":1554464161000},"page":"372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1181-546X","authenticated-orcid":false,"given":"Zhongjun","family":"Ma","sequence":"first","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8611-1101","authenticated-orcid":false,"given":"Shengwu","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Chen","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Jiangfeng","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Guangjie","family":"Li","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Shuangshuang","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Xiuyu","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s10346-014-0521-x","article-title":"Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh","volume":"12","author":"Ahmed","year":"2014","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s10064-015-0759-0","article-title":"Modification of seed cell sampling strategy for landslide susceptibility mapping: An application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey)","volume":"75","author":"Dagdelenler","year":"2015","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geoderma.2017.06.020","article-title":"Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques","volume":"305","author":"Chen","year":"2017","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.enggeo.2010.09.009","article-title":"Landslide susceptibility mapping in Injae, Korea, using a decision tree","volume":"116","author":"Yeon","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s10346-016-0744-0","article-title":"Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)","volume":"14","author":"Abdulwahid","year":"2016","journal-title":"Landslides"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.enggeo.2008.03.016","article-title":"Applicability of landslide susceptibility and hazard zoning at different scales","volume":"102","author":"Cascini","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s12665-011-0912-4","article-title":"Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey)","volume":"64","author":"Ercanoglu","year":"2011","journal-title":"Environ. Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s10346-011-0283-7","article-title":"A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at izmir, Turkey","volume":"9","author":"Akgun","year":"2012","journal-title":"Landslides"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China","volume":"115","author":"Bai","year":"2010","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1126\/science.1143308","article-title":"Assessing Landslide Hazards","volume":"316","author":"Keefer","year":"2007","journal-title":"Science"},{"key":"ref_12","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 multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s12665-012-1842-5","article-title":"Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea","volume":"68","author":"Park","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.scitotenv.2017.02.188","article-title":"Mapping landslide susceptibility using data-driven methods","volume":"589","author":"Zezere","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"5197","DOI":"10.1007\/s12665-014-3389-0","article-title":"Landslide susceptibility mapping by comparing the WLC and WofE multi-criteria methods in the West Crete Island, Greece","volume":"72","author":"Kouli","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., and Agliardi, F. (2014). Recommendations for the quantitative analysis of landslide risk. Bull. Eng. Geol. Environ.","DOI":"10.1007\/s10064-013-0538-8"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.enggeo.2004.08.005","article-title":"Landslide inventory of northwestern Anatolia, Turkey","volume":"77","author":"Duman","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1007\/s11629-013-2847-6","article-title":"Landslide susceptibility mapping along Bhalubang\u2014Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models","volume":"11","author":"Regmi","year":"2014","journal-title":"J. Mt. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.proeps.2015.08.059","article-title":"Sinkhole Susceptibility Mapping Using a Frequency Ratio Method and GIS Technology Near Karap\u0131nar, Konya-Turkey","volume":"15","author":"Ozdemir","year":"2015","journal-title":"Procedia Earth Planet. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10346-016-0771-x","article-title":"A modified frequency ratio method for landslide susceptibility assessment","volume":"14","author":"Li","year":"2016","journal-title":"Landslides"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"270","DOI":"10.3390\/w8070270","article-title":"Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China","volume":"8","author":"Cao","year":"2016","journal-title":"Water"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"172","DOI":"10.3390\/ijgi6060172","article-title":"Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China","volume":"6","author":"Wang","year":"2017","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_25","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\u2013Narayanghat road section in Nepal Himalaya","volume":"65","author":"Devkota","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"133","DOI":"10.5194\/nhess-4-133-2004","article-title":"Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal)","volume":"4","author":"Reis","year":"2004","journal-title":"Nat. Hazards Earth Syst. Sci. Discuss."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s10346-014-0550-5","article-title":"A systematic review of landslide probability mapping using logistic regression","volume":"12","author":"Budimir","year":"2015","journal-title":"Landslides"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4818","DOI":"10.1109\/JSTARS.2014.2337273","article-title":"A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling","volume":"7","author":"Elkadiri","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.enggeo.2006.09.006","article-title":"Logistic Regression analysis in the evaluation of mass movements susceptibility: The Aspromonte case study, Calabria, Italy","volume":"89","author":"Greco","year":"2007","journal-title":"Eng. Geol."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10346-006-0047-y","article-title":"Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models","volume":"4","author":"Lee","year":"2007","journal-title":"Landslides"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s10346-007-0088-x","article-title":"Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: Case study of Youngin, Korea","volume":"4","author":"Lee","year":"2007","journal-title":"Landslides"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115","DOI":"10.5194\/nhess-6-115-2006","article-title":"Landslide hazard assessment in the Collazzone area, Umbria, Central Italy","volume":"6","author":"Guzzetti","year":"2006","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.enggeo.2008.03.004","article-title":"Statistical approach to earthquake-induced landslide susceptibility","volume":"100","author":"Lee","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.geomorph.2016.02.012","article-title":"Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models","volume":"259","author":"Hong","year":"2016","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s12665-015-4950-1","article-title":"Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran","volume":"75","author":"Pourghasemi","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2015","journal-title":"Landslides"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.geomorph.2009.09.026","article-title":"Channel initiation by surface and subsurface flows in a steep catchment of the Akaishi Mountains, Japan","volume":"115","author":"Imaizumi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cao, C., Xu, P., Chen, J., Zheng, L., and Niu, C. (2016). Hazard Assessment of Debris-Flow along the Baicha River in Heshigten Banner, Inner Mongolia, China. Int. J. Environ. Res. Public Health, 14.","DOI":"10.3390\/ijerph14010030"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Oh, H.-J., and Lee, S. (2017). Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree. Appl. Sci., 7.","DOI":"10.3390\/app7101000"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lee, S., Lee, M.-J., and Jung, H.-S. (2017). Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea. Appl. Sci., 7.","DOI":"10.3390\/app7070683"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.cageo.2008.08.007","article-title":"Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat\u2014Turkey)","volume":"35","author":"Yilmaz","year":"2009","journal-title":"Comput. Geosci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7501","DOI":"10.1002\/2015WR016909","article-title":"An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions","volume":"51","author":"Anagnostopoulos","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/S0022-1694(02)00118-X","article-title":"Analysis of topographic and climatic control on rainfall-triggered shallow landsliding using a quasi-dynamic wetness index","volume":"268","author":"Borga","year":"2002","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ma, Z., Qin, S., Chen, J., Lv, J., Chen, J., and Zhao, X. (2017). A probabilistic method for evaluating wedge stability based on blind data theory. Bull. Eng. Geol. Environ.","DOI":"10.1007\/s10064-017-1204-3"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.jhydrol.2016.03.036","article-title":"Estimating return period of landslide triggering by Monte Carlo simulation","volume":"541","author":"Peres","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.jhydrol.2018.10.036","article-title":"Modeling impacts of climate change on return period of landslide triggering","volume":"567","author":"Peres","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.enggeo.2008.03.013","article-title":"Modeling landslide recurrence in Seattle, Washington, USA","volume":"102","author":"Salciarini","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2017.04.013","article-title":"Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine","volume":"223","author":"Huang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1007\/s10346-015-0657-3","article-title":"Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: A case study in the upper Lo River catchment (Vietnam)","volume":"13","author":"Hung","year":"2015","journal-title":"Landslides"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10064-017-1010-y","article-title":"A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China","volume":"77","author":"Chen","year":"2017","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s12145-018-0335-9","article-title":"A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment","volume":"11","author":"Ba","year":"2018","journal-title":"Earth Sci. Inform."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/S1367-9120(03)00019-1","article-title":"Late Pliocene\u2013recent tectonic setting for the Tianchi volcanic zone, Changbai Mountains, northeast China","volume":"21","author":"Wang","year":"2003","journal-title":"J. Asian Earth Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.geoderma.2016.03.021","article-title":"Black carbon deposition and storage in peat soils of the Changbai Mountain, China","volume":"273","author":"Gao","year":"2016","journal-title":"Geoderma"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.trd.2016.10.030","article-title":"Monitoring wildlife crossing structures along highways in Changbai Mountain, China","volume":"50","author":"Wang","year":"2017","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1007\/s11430-006-0304-x","article-title":"Effect of gas emissions from Tianchi volcano (NE China) on environment and its potential volcanic hazards","volume":"49","author":"Guo","year":"2006","journal-title":"Sci. China Ser. D"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11629-016-4126-9","article-title":"Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China","volume":"14","author":"Du","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/BF01001956","article-title":"Rough sets","volume":"11","author":"Pawlak","year":"1982","journal-title":"Int. J. Comput. Inf. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.geomorph.2013.08.013","article-title":"Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China","volume":"204","author":"Peng","year":"2014","journal-title":"Geomorphology"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/0378-4754(78)90064-2","article-title":"Modeling unstructured decision problems-the theory of analytical hierarchies","volume":"20","author":"Saaty","year":"1978","journal-title":"Math. Comput. Simul."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.renene.2017.10.044","article-title":"Large scale PV sites selection by combining GIS and Analytical Hierarchy Process. Case study: Eastern Morocco","volume":"119","author":"Mezrhab","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s12517-015-2258-9","article-title":"Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran","volume":"9","author":"Sangchini","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_64","first-page":"1185","article-title":"Spatial Mapping of Groundwater Potential Using Entropy Weighted Linear Aggregate Novel Approach and GIS","volume":"42","author":"Pourghasemi","year":"2016","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1007\/s12665-016-5580-y","article-title":"GIS based frequency ratio and index of entropy models to landslide susceptibility mapping (Daguan, China)","volume":"75","author":"Wang","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhao, H., Yao, L., Mei, G., Liu, T., and Ning, Y. (2017). A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map. Entropy, 19.","DOI":"10.3390\/e19080396"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s11069-012-0414-z","article-title":"Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China)","volume":"65","author":"Xu","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s10346-015-0565-6","article-title":"Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece","volume":"13","author":"Tsangaratos","year":"2015","journal-title":"Landslides"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.geomorph.2009.06.020","article-title":"Optimal landslide susceptibility zonation based on multiple forecasts","volume":"114","author":"Rossi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.enggeo.2016.11.001","article-title":"Rainfall-based landslide susceptibility analysis for natural terrain in Hong Kong\u2014A direct stock-taking approach","volume":"215","author":"Ko","year":"2016","journal-title":"Eng. Geol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1016\/j.cageo.2011.02.010","article-title":"A rough set approach to analyze factors affecting landslide incidence","volume":"37","author":"Liu","year":"2011","journal-title":"Comput. Geosci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1007\/s11069-012-0463-3","article-title":"GIS-multicriteria decision analysis for landslide susceptibility mapping: Comparing three methods for the Urmia lake basin, Iran","volume":"65","author":"Feizizadeh","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.geomorph.2014.12.042","article-title":"Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics","volume":"234","author":"Meinhardt","year":"2015","journal-title":"Geomorphology"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s12665-017-6689-3","article-title":"A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS","volume":"76","author":"Pham","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s10064-015-0734-9","article-title":"Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia","volume":"75","author":"Youssef","year":"2015","journal-title":"Bull. Eng. Geol. Environ."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/4\/372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:43:14Z","timestamp":1760186594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/4\/372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,5]]},"references-count":77,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["e21040372"],"URL":"https:\/\/doi.org\/10.3390\/e21040372","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,5]]}}}