 GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of landslide susceptibility mappingHassan Abedi Gheshlaghi () and
Bakhtiar Feizizadeh ()
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 107, issue 2, No 45, 2014 pages Abstract: Abstract The primary objective is to propose and verify an ensemble approach based on fuzzy system and bivariate statistics for landslide susceptibility assessment (LSA) at Azarshahr Chay Basin (Iran). In this regard, various integrations of fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with index of entropy (IOE) were investigated. Aerial photograph interpretations and substantial field checking were used to identify the landslide locations. Out of 75 identified landslides, 52 (≈70%) locations were utilized for the training of the models, whereas the remaining 23 (≈30%) cases were employed for the validation of the models. Fourteen landslide conditioning factors including altitude, slope aspect, slope degree, lithology, distance to fault, curvature, land use, distance to river, topographic position index (TPI), topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), distance to road, and rainfall were prepared and utilized during the analysis. The $$\mathrm{FMV}\_\mathrm{IOE}$$ FMV _ IOE , $$\mathrm{FR}\_\mathrm{IOE}$$ FR _ IOE , and $$\mathrm{IV}\_\mathrm{IOE}$$ IV _ IOE models were designed utilizing the dataset for training. Finally, to validate as well as to compare the model’s predictive abilities, the statistical measures of receiver operating characteristic (ROC), including sensitivity, accuracy, and specificity, were employed. The accuracy of 92.7, 92.5, and 91.8% of the models such as $$\mathrm{FMV}\_\mathrm{IOE}$$ FMV _ IOE , $$\mathrm{FR}\_\mathrm{IOE}$$ FR _ IOE , and $$\mathrm{IV}\_\mathrm{IOE}$$ IV _ IOE ensembles, respectively, was by the area under the receiver operating characteristic (AUROC) values developed from the ROC curve. For the validation dataset, the $$\mathrm{FMV}\_\mathrm{IOE}$$ FMV _ IOE model had the maximum sensitivity, accuracy, and specificity values of 95.7, 91.3, and 87.0%, respectively. Thus, the ensemble of FMV_IOE was introduced as a promising and premier approach that could be used for LSA in the study area. Also, IOE results indicated that altitude, lithology, and slope degree were main drivers of landslide occurrence. The results of the present research can be employed as a platform for appropriate basined management practices in order to plan the highly susceptible zones to landslide and hence minimize the expected losses. Keywords: Landslide susceptibility; Conditioning factors; Ensemble approach; GIS (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:107:y:2021:i:2:d:10.1007_s11069-021-04673-1 Ordering information: This journal article can be ordered from DOI: 10.1007/s11069-021-04673-1 Access Statistics for this article Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards |
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