{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:54:29Z","timestamp":1770270869499,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T00:00:00Z","timestamp":1601337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the \u201coptimized\u201d GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH\u2013GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH\u2013GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively.<\/jats:p>","DOI":"10.3390\/ijgi9100566","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T20:56:22Z","timestamp":1601412982000},"page":"566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model"],"prefix":"10.3390","volume":"9","author":[{"given":"Azam","family":"Kadirhodjaev","sequence":"first","affiliation":[{"name":"Deputy Chairman of the State Committee on Geology and Mineral Resources, 11, T. Shevchenko, Tashkent 100060, Uzbekistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1771-6753","authenticated-orcid":false,"given":"Fatemeh","family":"Rezaie","sequence":"additional","affiliation":[{"name":"Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea"},{"name":"Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1226-3460","authenticated-orcid":false,"given":"Moung-Jin","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Environmental Data Strategy, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong-si 30147, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0409-8263","authenticated-orcid":false,"given":"Saro","family":"Lee","sequence":"additional","affiliation":[{"name":"Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea"},{"name":"Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"ref_1","first-page":"5","article-title":"Development of GIS based landslide information system for the region of East Sikkim","volume":"49","author":"Chakraborty","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4155\/cmt.11.18","article-title":"Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change","volume":"2","author":"Goetz","year":"2011","journal-title":"Carbon Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nohani, E., Moharrami, M., Sharafi, S., Khosravi, K., Pradhan, B., Pham, B.T., Lee, S., and Melesse, A.M. (2019). Landslide susceptibility mapping using different GIS-based bivariate models. Water, 11.","DOI":"10.3390\/w11071402"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s12303-014-0065-z","article-title":"Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS","volume":"19","author":"Youssef","year":"2015","journal-title":"Geosci. J."},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s11069-012-0321-3","article-title":"A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran","volume":"64","author":"Shirzadi","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s11069-012-0217-2","article-title":"Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran","volume":"63","author":"Pourghasemi","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.enggeo.2006.02.003","article-title":"Analytic network process model for landslide hazard zonation","volume":"85","author":"Neaupane","year":"2006","journal-title":"Eng. Geol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/S0377-2217(03)00020-1","article-title":"Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS","volume":"156","author":"Opricovic","year":"2004","journal-title":"Eur. J. Oper. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/19475705.2013.871353","article-title":"Identification of natural hazards and classification of urban areas by TOPSIS model (case study: Bandar Abbas city, Iran)","volume":"7","author":"Najafabadi","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1016\/j.scitotenv.2018.06.130","article-title":"A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment","volume":"642","author":"Khosravi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.jhydrol.2019.03.073","article-title":"A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods","volume":"573","author":"Khosravi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"124774","DOI":"10.1016\/j.jhydrol.2020.124774","article-title":"Bedload transport rate prediction: Application of novel hybrid data mining techniques","volume":"585","author":"Khosravi","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_17","unstructured":"Ngo, P.T.T., Panahi, M., Khosravi, K., Ghorbanzadeh, O., Karimnejad, N., Cerda, A., and Lee, S. (2020). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci. Front."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4763","DOI":"10.1080\/01431160701264227","article-title":"Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea","volume":"28","author":"Lee","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pascale, S., Parisi, S., Mancini, A., Schiattarella, M., Conforti, M., Sole, A., Murgante, B., and Sdao, F. (2013, January 24\u201327). Landslide susceptibility mapping using artificial neural network in the urban area of Senise and San Costantino Albanese (Basilicata, Southern Italy). Proceedings of the International Conference on Computational Science and Its Applications, Ho Chi Minh City, Vietnam.","DOI":"10.1007\/978-3-642-39649-6_34"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104225","DOI":"10.1016\/j.catena.2019.104225","article-title":"Landslide susceptibility hazard map in southwest Sweden using artificial neural network","volume":"183","author":"Shahri","year":"2019","journal-title":"Catena"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.scitotenv.2017.09.293","article-title":"River suspended sediment modelling using the CART model: A comparative study of machine learning techniques","volume":"615","author":"Choubin","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lee, S., Panahi, M., Pourghasemi, H.R., Shahabi, H., Alizadeh, M., Shirzadi, A., Khosravi, K., Melesse, A.M., Yekrangnia, M., and Rezaie, F. (2019). Sevucas: A novel gis-based machine learning software for seismic vulnerability assessment. Appl. Sci., 9.","DOI":"10.3390\/app9173495"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"139937","DOI":"10.1016\/j.scitotenv.2020.139937","article-title":"Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms","volume":"741","author":"Panahi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1007\/s10064-017-1125-1","article-title":"Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area","volume":"78","author":"Polykretis","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"137612","DOI":"10.1016\/j.scitotenv.2020.137612","article-title":"Improving prediction of water quality indices using novel hybrid machine-learning algorithms","volume":"721","author":"Bui","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11004-011-9379-9","article-title":"Support vector machines for landslide susceptibility mapping: The Staffora River Basin case study, Italy","volume":"44","author":"Ballabio","year":"2012","journal-title":"Math. Geosci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lee, S., Hong, S.-M., and Jung, H.-S. (2017). A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability, 9.","DOI":"10.3390\/su9010048"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s40710-017-0248-5","article-title":"Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area, Uttarakhand, India","volume":"4","author":"Pham","year":"2017","journal-title":"Environ. Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1080\/10106049.2018.1489422","article-title":"A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling","volume":"34","author":"Pham","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)","volume":"163","author":"Hong","year":"2018","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Shirzadi, A., Shahabi, H., Geertsema, M., Omidvar, E., Clague, J.J., Thai Pham, B., Dou, J., Talebpour Asl, D., and Bin Ahmad, B. (2019). New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed. Forests, 10.","DOI":"10.3390\/f10090743"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/s10346-020-01392-9","article-title":"Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability","volume":"17","author":"Carotenuto","year":"2020","journal-title":"Landslides"},{"key":"ref_35","first-page":"183","article-title":"Analysis of Geomorphologic Hazards of Landslide and Flood using VIKOR-AHP and Fr Models in the Alborz Province","volume":"51","author":"Khodadadi","year":"2019","journal-title":"Phys. Geogr. Res. Q."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A., Pirasteh, S., Alesheikh, A.A., Khosravi, K., and Panahi, S. (2019). Spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo). Appl. Sci., 9.","DOI":"10.3390\/app9183755"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.2166\/wst.2016.064","article-title":"A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes","volume":"73","author":"Ebtehaj","year":"2016","journal-title":"Water Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.amc.2017.06.012","article-title":"Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design","volume":"313","author":"Shaghaghi","year":"2017","journal-title":"Appl. Math. Comput."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"125423","DOI":"10.1016\/j.jhydrol.2020.125423","article-title":"Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search","volume":"590","author":"Dodangeh","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nguyen, V.V., Pham, B.T., Vu, B.T., Prakash, I., Jha, S., Shahabi, H., Shirzadi, A., Ba, D.N., Kumar, R., and Chatterjee, J.M. (2019). Hybrid machine learning approaches for landslide susceptibility modeling. Forests, 10.","DOI":"10.3390\/f10020157"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shirzadi, A., Soliamani, K., Habibnejhad, M., Kavian, A., Chapi, K., Shahabi, H., Chen, W., Khosravi, K., Thai Pham, B., and Pradhan, B. (2018). Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors, 18.","DOI":"10.3390\/s18113777"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.gsf.2020.07.012","article-title":"Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer","volume":"12","author":"Chen","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_44","first-page":"1","article-title":"A novel hybrid approach of landslide susceptibility modeling using rotation forest ensemble and different base classifiers","volume":"14","author":"Prakash","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1177\/0309133314528944","article-title":"Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland: Do paraglacial factors vary over space?","volume":"38","author":"Feuillet","year":"2014","journal-title":"Prog. Phys. Geogr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1130\/B30625.1","article-title":"The effects of precipitation gradients on river profile evolution on the Big Island of Hawai\u2019i","volume":"125","author":"Menking","year":"2013","journal-title":"GSA Bull."},{"key":"ref_47","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_48","first-page":"43","article-title":"The group method of data of handling; a rival of the method of stochastic approximation","volume":"13","author":"Ivakhnenko","year":"1968","journal-title":"Sov. Autom. Control"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112099","DOI":"10.1016\/j.enconman.2019.112099","article-title":"A novel wind speed prediction method based on robust local mean decomposition, group method of data handling and conditional kernel density estimation","volume":"200","author":"Jiang","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"101682","DOI":"10.1016\/j.bspc.2019.101682","article-title":"Blind, Cuff-less, Calibration-Free and Continuous Blood Pressure Estimation using Optimized Inductive Group Method of Data Handling","volume":"57","author":"Mohebbian","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.atmosenv.2012.01.051","article-title":"Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling","volume":"51","author":"Zhu","year":"2012","journal-title":"Atmos. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.ijheatmasstransfer.2018.09.057","article-title":"Modeling heat capacity of ionic liquids using group method of data handling: A hybrid and structure-based approach","volume":"129","author":"Rostami","year":"2019","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"106269","DOI":"10.1016\/j.ijepes.2020.106269","article-title":"Wavelet group method of data handling for fault prediction in electrical power insulators","volume":"123","author":"Stefenon","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_54","first-page":"406","article-title":"Group method of data handling to predict scour depth around vertical piles under regular waves","volume":"20","author":"Najafzadeh","year":"2013","journal-title":"Sci. Iran."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1080\/0952813X.2018.1554712","article-title":"An opposition-based chaotic grey wolf optimizer for global optimisation tasks","volume":"31","author":"Gupta","year":"2019","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"78012","DOI":"10.1109\/ACCESS.2019.2921793","article-title":"Hierarchy strengthened grey wolf optimizer for numerical optimization and feature selection","volume":"7","author":"Tu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.atmosenv.2016.03.056","article-title":"A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2. 5 concentration forecasting","volume":"134","author":"Niu","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.engappai.2017.10.024","article-title":"An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization","volume":"68","author":"Long","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s00366-017-0567-1","article-title":"Improved GWO algorithm for optimal design of truss structures","volume":"34","author":"Kaveh","year":"2018","journal-title":"Eng. Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"105645","DOI":"10.1016\/j.asoc.2019.105645","article-title":"A binary grey wolf optimizer for the multidimensional knapsack problem","volume":"83","author":"Luo","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1023\/B:NHAZ.0000007172.62651.2b","article-title":"Validation of spatial prediction models for landslide hazard mapping","volume":"30","author":"Chung","year":"2003","journal-title":"Nat. Hazards"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ecoinf.2017.12.006","article-title":"Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers","volume":"43","author":"Jaafari","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"5453","DOI":"10.1007\/s10661-011-2352-8","article-title":"Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey)","volume":"184","author":"Akgun","year":"2012","journal-title":"Environ. Monit. Assess."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/10\/566\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:15:02Z","timestamp":1760177702000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/10\/566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,29]]},"references-count":65,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["ijgi9100566"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9100566","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,29]]}}}