Abstract
Landslide susceptibility maps are modelled either by knowledge-driven methods (qualitative) or data-driven methods (quantitative) depending on apriori expert knowledge or complete landslide inventory data, respectively, which hinders land regulatory authorities from taking quick decisions for land use planning at a regional scale. To overcome these limitations, a new modelling methodology is proposed which can perform equally with the existing established models and needs minimum knowledge of geo-environmental variables without any model testing data. In this method, common variances of the geo-environmental variables are extracted from initially equally weighted variables and are termed as equal weight method (EWM). The extracted common variances are used to weight the variables for the preparation of a landslide susceptibility map on a regional scale. The output of the model can be interpreted to understand the inter-relationship of different geo-environmental variables along with the identification and prioritization of variables on model performance by delineating the non-performing variables. The proposed modelling methodology is tested at the eastern Himalayan road corridor, the result of which is compared with the results of the analytical hierarchy process (qualitative) and frequency ratio (quantitative) modelling methods by area under curve (AUC) of receiver operating characteristic (ROC) curves and cost analysis. AUC of ROC curves for the analytical hierarchy process, frequency ratio method and equal weight method models are 0.755, 0.809 and 0.774, respectively, indicating little performance differences. Cost analysis of all models shows a maximum bounding cost of 0.4 indicating that all models are cost-effective. The study concludes that the equal weight method is quick, robust and cost-effective at par with other well tested knowledge-driven and data-driven methods.
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The authors are grateful to National Mission Head, III, GSI, for kind permission to publish the paper. The authors are also indebted to the anonymous reviewers for helpful suggestions to upgrade the contribution.
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SKS contributed mainly to the research work by developing a landslide study model and preparing the manuscript's first draft. GIS inputs and model comparisons are from SG, SB and SDG. TK, JNH, MM, and PD were involved in field data generation and compilation.
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Communicated by Aparna Shukla
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Som, S.K., Ghosh, S., Dasgupta, S. et al. Utility of common variance of equally-weighted variables for GIS-based landslide susceptibility mapping at the eastern Himalaya. J Earth Syst Sci 132, 16 (2023). https://doi.org/10.1007/s12040-022-02017-6
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DOI: https://doi.org/10.1007/s12040-022-02017-6