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    • Keywords


      Collapse susceptibility map; gypsum; GIS; bivariate (conditional probability); multivariate (logistic regression); soft computing (artificial neural networks).

    • Abstract


      The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

    • Author Affiliations


      Işık Yilmaz1 Marian Marschalko2 Martin Bednarik3

      1. Department of Geological Engineering, Cumhuriyet University, 58140 Sivas, Turkey.
      2. Institute of Geological Engineering, VˇSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
      3. Department of Engineering Geology, Comenius University, Mlynsk´a dolina, 842 15 Bratislava, Slovak Republic.
    • Dates

  • Journal of Earth System Science | News

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      Posted on July 25, 2019

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