Analysis and Validation of Geo-environmental Susceptibility for Landslide Occurrences Using Frequency Ratio and Evidential Belief Function – A Case for Landslides in Chuncheon in 2013 –

2020 ◽  
Vol 37 (1) ◽  
pp. 61-89
Author(s):  
Won Young Lee ◽  
Hyo Hyun Sung ◽  
Sejin Ahn ◽  
Seon Ki Park
Keyword(s):  
Frequency Ratio ◽  
Belief Function ◽  
Evidential Belief Function

scholarly journals Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors

2018 ◽  
Vol 20 (6) ◽  
pp. 1436-1451 ◽  
Author(s):  
Jeong-Cheol Kim ◽  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Frequency Ratio ◽  
Groundwater Resources ◽  
Specific Capacity ◽  
Belief Function ◽  
Neural Network Models ◽  
Evidential Belief Function ◽  
Artificial Neural ◽  
Productivity Potential

Abstract This study analysed groundwater productivity potential (GPP) using three different models in a geographic information system (GIS) for Okcheon city, Korea. Specifically, we have used variety topography factors in this study. The models were based on relationships between groundwater productivity (for specific capacity (SPC) and transmissivity (T)) and hydrogeological factors. Topography, geology, lineament, land-use and soil data were first collected, processed and entered into the spatial database. T and SPC data were collected from 86 well locations. The resulting GPP map has been validated in under the curve analysis area using well data not used for model training. The GPP maps using artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) models for T had accuracies of 82.19%, 81.15% and 80.40%, respectively. Similarly, the ANN, FR and EBF models for SPC had accuracies of 81.67%, 81.36% and 79.89%, respectively. The results illustrate that ANN models can be useful for the development of groundwater resources.

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