{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:05:03Z","timestamp":1760709903140,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000943","name":"Commonwealth Scientific and Industrial Research Organisation","doi-asserted-by":"publisher","award":["RT109121"],"award-info":[{"award-number":["RT109121"]}],"id":[{"id":"10.13039\/501100000943","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics\u2019 area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state\/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.<\/jats:p>","DOI":"10.3390\/s19102274","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T11:21:22Z","timestamp":1558005682000},"page":"2274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Rule-Based Approach in Mapping Landslide Susceptibility"],"prefix":"10.3390","volume":"19","author":[{"given":"Majid","family":"Roodposhti","sequence":"first","affiliation":[{"name":"Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Churchill Ave, Hobart, TAS 7005, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2127","authenticated-orcid":false,"given":"Jagannath","family":"Aryal","sequence":"additional","affiliation":[{"name":"Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Churchill Ave, Hobart, TAS 7005, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, NSW 2007, Australia"},{"name":"Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdongro Gwangjin-gu, Seoul 05006, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1109\/TGRS.2010.2050328","article-title":"Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia)","volume":"48","author":"Pradhan","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.jhydrol.2014.03.008","article-title":"Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS","volume":"512","author":"Tehrany","year":"2014","journal-title":"J. Hydrol."},{"doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Aryal, J., and Gholaminia, K. (2018). A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J. Spat. Sci., 1\u201317.","key":"ref_4","DOI":"10.1080\/14498596.2018.1505564"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1023\/A:1008097111310","article-title":"Use of GIS technology in the prediction and monitoring of landslide hazard","volume":"20","author":"Carrara","year":"1999","journal-title":"Nat. Hazards"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0013-7952(03)00069-3","article-title":"Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA","volume":"69","author":"Ohlmacher","year":"2003","journal-title":"Eng. Geol."},{"key":"ref_8","first-page":"209","article-title":"Recommendations for the quantitative analysis of landslide risk","volume":"73","author":"Corominas","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.geomorph.2017.07.010","article-title":"An integrated approach to earthquake-induced landslide hazard zoning based on probabilistic seismic scenario for Phlegrean Islands (Ischia, Procida and Vivara), Italy","volume":"295","author":"Caccavale","year":"2017","journal-title":"Geomorphology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s12517-018-3531-5","article-title":"Analysis and evaluation of landslide susceptibility: A review on articles published during 2005\u20132016 (periods of 2005\u20132012 and 2013\u20132016)","volume":"11","author":"Pourghasemi","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11069-012-0523-8","article-title":"PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping","volume":"73","author":"Roodposhti","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.cageo.2014.08.001","article-title":"A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping","volume":"73","author":"Feizizadeh","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_13","first-page":"49","article-title":"Integrating GIS Based Fuzzy Set Theory in Multicriteria Evaluation Methods for Landslide Susceptibility Mapping","volume":"9","author":"Feizizadeh","year":"2013","journal-title":"Int. J. Geoinform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S0013-7952(02)00241-7","article-title":"Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach","volume":"68","author":"Zhou","year":"2003","journal-title":"Eng. Geol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/(SICI)1096-9837(199609)21:9<853::AID-ESP676>3.0.CO;2-C","article-title":"An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia)","volume":"21","author":"Westen","year":"1996","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.enggeo.2005.02.002","article-title":"Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)","volume":"79","author":"Yesilnacar","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_18","first-page":"9","article-title":"Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain","volume":"2","author":"Barredo","year":"2000","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geomorph.2006.10.032","article-title":"Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria)","volume":"94","author":"Ruff","year":"2008","journal-title":"Geomorphology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1007\/s11069-013-0857-x","article-title":"Landslide susceptibility deterministic approach using geographic information systems: application to Breaza town, Romania","volume":"70","author":"Vartolomei","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geomorph.2014.07.026","article-title":"Landslide susceptibility mapping using geographically-weighted principal component analysis","volume":"226","author":"Sabokbar","year":"2014","journal-title":"Geomorphology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0169-555X(02)00083-1","article-title":"Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries","volume":"47","year":"2002","journal-title":"Geomorphology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s10346-009-0166-3","article-title":"A statistical assessment on international landslide literature (1945\u20132008)","volume":"6","author":"Gokceoglu","year":"2009","journal-title":"Landslides"},{"doi-asserted-by":"crossref","unstructured":"Kadavi, P., Lee, C.-W., and Lee, S. (2018). Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens., 10.","key":"ref_24","DOI":"10.3390\/rs10081252"},{"doi-asserted-by":"crossref","unstructured":"Park, S.-J., Lee, C.-W., Lee, S., and Lee, M.-J. (2018). Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea. Remote Sens., 10.","key":"ref_25","DOI":"10.3390\/rs10101545"},{"doi-asserted-by":"crossref","unstructured":"Hjort, J., and Luoto, M. (2013). Statistical methods for geomorphic distribution modeling. Treatise on Geomorphology, Academic Press.","key":"ref_26","DOI":"10.1016\/B978-0-12-374739-6.00028-2"},{"key":"ref_27","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_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.geomorph.2018.10.024","article-title":"A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model","volume":"327","author":"Yan","year":"2019","journal-title":"Geomorphology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.envsoft.2018.10.006","article-title":"A novel algorithm for calculating transition potential in cellular automata models of land-use\/cover change","volume":"112","author":"Roodposhti","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1007\/s12517-017-2918-z","article-title":"Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping","volume":"10","author":"Feizizadeh","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_33","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."},{"unstructured":"Middlemann, M.H., and Middelmann, M. (2007). Natural Hazards in Australia: Identifying Risk Analysis Requirements.","key":"ref_34"},{"unstructured":"MRT (2018, July 12). Mineral Resources Tasmania. Landslides, Available online: http:\/\/www.mrt.tas.gov.au\/portal\/landslides.","key":"ref_35"},{"unstructured":"Kiernan, K. (1990). Geomorphology Manual.","key":"ref_36"},{"unstructured":"Mazengarb, C., and Stevenson, M. (2010). Tasmanian Landslide Map Series: User Guide and Technical Methodology.","key":"ref_37"},{"key":"ref_38","first-page":"39","article-title":"Grasping the nettle: The Tasmanian geological survey\u2019s work on landslides, 1971\u20131988","volume":"145","author":"Stevenson","year":"2011","journal-title":"Pap. Proc. R. Soc. Tasman."},{"doi-asserted-by":"crossref","unstructured":"Shadman Roodposhti, M., Aryal, J., Shahabi, H., and Safarrad, T. (2016). Fuzzy shannon entropy: a hybrid GIS-based landslide susceptibility mapping method. Entropy, 18.","key":"ref_39","DOI":"10.20944\/preprints201608.0032.v1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s12040-016-0686-x","article-title":"A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models","volume":"125","author":"Wang","year":"2016","journal-title":"J. Earth Syst. Sci."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.cageo.2011.10.031","article-title":"Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS","volume":"45","author":"Bui","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.catena.2016.06.004","article-title":"Comparison of a logistic regression and Na\u00efve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size","volume":"145","author":"Tsangaratos","year":"2016","journal-title":"Catena"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1023\/B:NHAZ.0000007169.28860.80","article-title":"A GIS-Based Multivariate Statistical Analysis for Shallow Landslide Susceptibility Mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain)","volume":"30","author":"Santacana","year":"2003","journal-title":"Nat. Hazards"},{"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":"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_47","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2005.02.002","article-title":"Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards","volume":"59","author":"Tralli","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.enggeo.2006.03.004","article-title":"A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas","volume":"85","author":"Kanungo","year":"2006","journal-title":"Eng. Geol."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s00703-007-0262-7","article-title":"Rainfall thresholds for the initiation of landslides in central and southern Europe","volume":"98","author":"Guzzetti","year":"2007","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1007\/s00254-001-0454-2","article-title":"Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach","volume":"41","author":"Ercanoglu","year":"2002","journal-title":"Environ. Geol."},{"unstructured":"Syme, G., Hatton MacDonald, D., Fulton, B., and Piantadosi, J. (2017, January 3\u20138). DoTRules: A novel method for calibrating land-use\/cover change models using a Dictionary of Trusted Rules. Proceedings of the MODSIM2017: 22nd International Congress on Modelling and Simulation, Hobart, Australia.","key":"ref_52"},{"unstructured":"R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing.","key":"ref_53"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/584091.584093","article-title":"A mathematical theory of communication","volume":"5","author":"Shannon","year":"2001","journal-title":"ACM SIGMOBILE Mob. Comput. Commun. Rev."},{"doi-asserted-by":"crossref","unstructured":"Shadman Roodposhti, M., Aryal, J., Lucieer, A., and Bryan, B.A. (2019). Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest. Entropy, 21.","key":"ref_55","DOI":"10.3390\/e21010078"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s12665-009-0373-1","article-title":"Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis","volume":"61","author":"Akgun","year":"2010","journal-title":"Environ. Earth. Sci"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"48","DOI":"10.4018\/ijaeis.2013100103","article-title":"The weighted fuzzy barycenter: Definition and application to forest fire control in the PACA region","volume":"4","author":"Josselin","year":"2013","journal-title":"Int. J. Agric. Environ. Inform. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3390\/e12010053","article-title":"Imprecise Shannon\u2019s entropy and multi attribute decision making","volume":"12","author":"Lotfi","year":"2010","journal-title":"Entropy"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2009.10.001","article-title":"A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses","volume":"110","author":"Nandi","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"doi-asserted-by":"crossref","unstructured":"Clague, J.J., and Stead, D. (2012). Landslides: Types, Mechanisms and Modeling, Cambridge University Press.","key":"ref_62","DOI":"10.1017\/CBO9780511740367"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00254-003-0917-8","article-title":"A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate","volume":"45","author":"Doyuran","year":"2004","journal-title":"Environ. Geol."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1007\/s10661-012-2666-1","article-title":"An expert-based approach to forest road network planning by combining Delphi and spatial multi-criteria evaluation","volume":"185","author":"Hayati","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.cageo.2012.11.003","article-title":"Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal","volume":"52","author":"Kayastha","year":"2013","journal-title":"Comput. Geosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2274\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:52:35Z","timestamp":1760187155000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,16]]},"references-count":67,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19102274"],"URL":"https:\/\/doi.org\/10.3390\/s19102274","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,5,16]]}}}