{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T03:32:29Z","timestamp":1775705549852,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,22]],"date-time":"2020-08-22T00:00:00Z","timestamp":1598054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30\u201390 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%\u201384%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.<\/jats:p>","DOI":"10.3390\/rs12172718","type":"journal-article","created":{"date-parts":[[2020,8,23]],"date-time":"2020-08-23T21:28:06Z","timestamp":1598218086000},"page":"2718","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Evaluating the Effects of Digital Elevation Models in Landslide Susceptibility Mapping in Rangamati District, Bangladesh"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4417-6580","authenticated-orcid":false,"given":"Yasin Wahid","family":"Rabby","sequence":"first","affiliation":[{"name":"Department of Geography, University of Tennessee, Knoxville, TN 37996, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2196-9764","authenticated-orcid":false,"given":"Asif","family":"Ishtiaque","sequence":"additional","affiliation":[{"name":"School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5540-3307","authenticated-orcid":false,"given":"Md. Shahinoor","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Sciences, New Jersey City University, Jersey City, NJ 07305, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.geomorph.2017.10.018","article-title":"Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models","volume":"301","author":"Marchesini","year":"2018","journal-title":"Geomorphology"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.enggeo.2008.03.022","article-title":"on behalf of the JTC-1 Joint Technical Committee on Landslides and Engineered Slopes (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning","volume":"102","author":"Fell","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/0143116031000156819","article-title":"An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas","volume":"25","author":"Arora","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.catena.2013.10.011","article-title":"A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping","volume":"114","author":"Althuwaynee","year":"2014","journal-title":"Catena"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1007\/s11629-016-4220-z","article-title":"Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: A combined approach of weights of evidence and spatial multi-criteria","volume":"14","author":"Rahman","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_9","unstructured":"Rahman, M.S., Ahmed, B., Huq, F.F., Rahman, S., and Al-Hussaini, T. (2016, January 21\u201323). Landslide inventory in an urban setting in the context of Chittagong Metropolitan Area, Bangladesh. Proceedings of the 3rd International Conference on Advances in Civil Engineering, Cox\u2019s Bazar Bangladesh."},{"key":"ref_10","first-page":"45","article-title":"Recommendations on a common approach for mapping areas at risk of landslides in Europe","volume":"23093","author":"Reichenbach","year":"2007","journal-title":"Guidel. Mapp. Areas Risk Landslides Eur. JRC Rep. EUR"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s100640050066","article-title":"Landslide hazard assessment: Summary review and new perspectives","volume":"58","author":"Aleotti","year":"1999","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.geomorph.2005.07.005","article-title":"A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia","volume":"74","author":"Komac","year":"2006","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1515\/geo-2016-0032","article-title":"Modeling of landslide volume estimation","volume":"8","author":"Amirahmadi","year":"2016","journal-title":"Open Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.clay.2011.01.015","article-title":"A geotechnical study on the landslides in the Trabzon Province, NE, Turkey","volume":"52","author":"Yalcin","year":"2011","journal-title":"Appl. Clay Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1080\/19475705.2016.1144655","article-title":"Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods","volume":"7","author":"Vakhshoori","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s12665-009-0394-9","article-title":"Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine","volume":"61","author":"Yilmaz","year":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.envsoft.2016.07.005","article-title":"A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)","volume":"84","author":"Pham","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ferentinou, M., and Chalkias, C. (2013). Mapping mass movement susceptibility across Greece with GIS, ANN and statistical methods. Landslide Science and Practice, Springer.","DOI":"10.1007\/978-3-642-31325-7_42"},{"key":"ref_19","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":"2016","journal-title":"Landslides"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10346-012-0317-9","article-title":"A regional scale quantitative risk assessment for landslides: Case of Kumluca watershed in Bartin, Turkey","volume":"10","author":"Erener","year":"2013","journal-title":"Landslides"},{"key":"ref_22","first-page":"399","article-title":"An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya, Turkey)","volume":"26","author":"San","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1080\/19475705.2018.1549111","article-title":"Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor","volume":"10","author":"Wang","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1029\/WS002p0195","article-title":"Validation of the shallow landslide model, SHALSTAB, for forest management","volume":"2","author":"Dietrich","year":"2001","journal-title":"Water Sci. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1002\/esp.1155","article-title":"DEM resolution effects on shallow landslide hazard and soil redistribution modelling","volume":"30","author":"Claessens","year":"2005","journal-title":"Earth Surf. Process. Landf. J. Br. Geomorphol. Res. Group"},{"key":"ref_26","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":"Bui","year":"2016","journal-title":"Landslides"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.5194\/nhess-13-2815-2013","article-title":"Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues","volume":"13","author":"Catani","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2344","DOI":"10.1785\/0120110233","article-title":"Density distribution of landslides triggered by the 2008 Wenchuan earthquake and their relationships to peak ground acceleration","volume":"103","author":"Yuan","year":"2013","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_29","first-page":"1","article-title":"Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques","volume":"9","author":"Chang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, X., Han, Z., and Dou, J. (2020). Spatial Proximity-Based Geographically Weighted Regression Model for Landslide Susceptibility Assessment: A Case Study of Qingchuan Area, China. Appl. Sci., 10.","DOI":"10.3390\/app10031107"},{"key":"ref_31","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":"2015","journal-title":"Landslides"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ahmed, B., and Dewan, A. (2017). Application of bivariate and multivariate statistical techniques in landslide susceptibility modeling in Chittagong City Corporation, Bangladesh. Remote Sens., 9.","DOI":"10.3390\/rs9040304"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s10346-018-1107-9","article-title":"An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping","volume":"16","author":"Rabby","year":"2019","journal-title":"Landslides"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rabby, Y.W., and Li, Y. (2020). Landslide Inventory (2001\u20132017) of Chittagong Hilly Areas, Bangladesh. Data, 5.","DOI":"10.20944\/preprints201911.0269.v1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sifa, S.F., Mahmud, T., Tarin, M.A., and Haque, D.M.E. (2019). Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: A case study of Rangamati district in Bangladesh. Geol. Ecol. Landsc., 1\u201314.","DOI":"10.1080\/24749508.2019.1619222"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ishtiaque, A., Masrur, A., Rabby, Y.W., Jerin, T., and Dewan, A. (2020). Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12040691"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1080\/19475705.2017.1403974","article-title":"Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)","volume":"8","author":"Hong","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_38","unstructured":"UNPO (2020, July 13). 2017, Chittagong Hill Tracts: Torrential Rainstorms and Wide-Scale Landslides Leave Thousands Homeless. Available online: https:\/\/unpo.org\/article\/20199?id=20199."},{"key":"ref_39","unstructured":"Bangladesh Bureau of Statistics (BBS) (2011). Population Census 2011."},{"key":"ref_40","unstructured":"Islam, M.A., Islam, M.S., and Islam, T. (2017, January 23). Landslides in Chittagong hill tracts and possible measures. Proceedings of the International Conference on Disaster Risk Mitigation, Dhaka, Bangladesh."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.geomorph.2015.07.035","article-title":"Generating landslide inventory by participatory mapping: An example in Purwosari Area, Yogyakarta, Java","volume":"306","author":"Samodra","year":"2018","journal-title":"Geomorphology"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.geomorph.2006.09.023","article-title":"Comparing landslide inventory maps","volume":"94","author":"Galli","year":"2008","journal-title":"Geomorphology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1080\/19475705.2016.1220023","article-title":"GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins","volume":"8","author":"Kanwal","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s12665-018-7524-1","article-title":"Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: A comparative study of Nojian watershed in Lorestan province, Iran","volume":"77","author":"Abedini","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1007\/s10346-019-01207-6","article-title":"Characteristics of landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake, Northern Japan","volume":"16","author":"Zhang","year":"2019","journal-title":"Landslides"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40677-019-0126-8","article-title":"Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India","volume":"6","author":"Roy","year":"2019","journal-title":"Geoenviron. Disasters"},{"key":"ref_47","unstructured":"Kafy, A.A., Rahman, M.S., and Ferdous, L. (2017, January 23). Exploring the association of land cover change and landslides in the Chittagong hill tracts (CHT): A remote sensing perspective. Proceedings of the International Conference on Disaster Risk Management, Dhaka, Bangladesh."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2019.01.221","article-title":"Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan","volume":"662","author":"Dou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10346-012-0380-2","article-title":"A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA","volume":"11","author":"Regmi","year":"2014","journal-title":"Landslides"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1080\/01431161.2016.1148282","article-title":"A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison","volume":"37","author":"Althuwaynee","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1007\/s00254-005-1228-z","article-title":"Probabilistic landslide susceptibility and factor effect analysis","volume":"47","author":"Lee","year":"2005","journal-title":"Environ. Geol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"104692","DOI":"10.1016\/j.envsoft.2020.104692","article-title":"Building a landslide hazard indicator with machine learning and land surface models","volume":"129","author":"Stanley","year":"2020","journal-title":"Environ. Model. Softw."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s40677-016-0053-x","article-title":"Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia","volume":"3","author":"Rasyid","year":"2016","journal-title":"Geoenviron. Disasters"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.enggeo.2008.01.004","article-title":"An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps","volume":"97","author":"Nefeslioglu","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geomorph.2012.03.036","article-title":"Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale","volume":"161","author":"Schicker","year":"2012","journal-title":"Geomorphology"},{"key":"ref_56","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_57","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_58","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_61","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/09720502.2010.10700699","article-title":"Collinearity diagnostics of binary logistic regression model","volume":"13","author":"Midi","year":"2010","journal-title":"J. Interdiscip. Math."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A caution regarding rules of thumb for variance inflation factors","volume":"41","year":"2007","journal-title":"Qual. Quant."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.catena.2017.05.016","article-title":"A comparative study between popular statistical and machine learning methods for simulating volume of landslides","volume":"157","author":"Shirzadi","year":"2017","journal-title":"Catena"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1093\/jee\/39.2.269","article-title":"Individual comparisons of grouped data by ranking methods","volume":"39","author":"Wilcoxon","year":"1946","journal-title":"J. Econ. Entomol."},{"key":"ref_67","unstructured":"Davis, J.C., and Sampson, R.J. (1986). Statistics and Data Analysis in Geology, Wiley."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, G., Lei, X., Chen, W., Shahabi, H., and Shirzadi, A. (2020). Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry, 12.","DOI":"10.3390\/sym12030325"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11069-018-3299-7","article-title":"Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models","volume":"93","author":"Polykretis","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_70","first-page":"1131","article-title":"How valid are social vulnerability models?","volume":"109","author":"Rufat","year":"2019","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_71","unstructured":"ASF (2020, July 13). ALOS PALSAR\u2014Radiometric Terrain Correction [online]. Available online: https:\/\/asf.alaska.edu\/data-sets\/derived-data-sets\/alos-palsar-rtc\/alos-palsar-radiometric-terrain-correction\/."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1016\/j.asej.2017.01.007","article-title":"Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia","volume":"9","author":"Elkhrachy","year":"2018","journal-title":"Ain Shams Eng. J."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.gloplacha.2006.11.036","article-title":"Evaluating digital elevation models for glaciologic applications: An example from Nevado Coropuna, Peruvian Andes","volume":"59","author":"Racoviteanu","year":"2007","journal-title":"Glob. Planet. Chang."},{"key":"ref_74","unstructured":"SOB (2020, July 13). Survey of Bangladesh [Online], Available online: http:\/\/www.sob.gov.bd\/site\/page\/76293334-a621\u20134508-b49f-a1c26af7ea3a\/Photogrammetric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2718\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:05:13Z","timestamp":1760177113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,22]]},"references-count":74,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12172718"],"URL":"https:\/\/doi.org\/10.3390\/rs12172718","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,22]]}}}