Version 1
: Received: 27 November 2023 / Approved: 28 November 2023 / Online: 28 November 2023 (07:18:26 CET)
How to cite:
Mamun, A.; Dong-Ho, J. Assessment of Landslide Risk in the Jinbu Area, Korea: Integrating Spatial Models with High Resolution Social Economic Data. Preprints2023, 2023111738. https://doi.org/10.20944/preprints202311.1738.v1
Mamun, A.; Dong-Ho, J. Assessment of Landslide Risk in the Jinbu Area, Korea: Integrating Spatial Models with High Resolution Social Economic Data. Preprints 2023, 2023111738. https://doi.org/10.20944/preprints202311.1738.v1
Mamun, A.; Dong-Ho, J. Assessment of Landslide Risk in the Jinbu Area, Korea: Integrating Spatial Models with High Resolution Social Economic Data. Preprints2023, 2023111738. https://doi.org/10.20944/preprints202311.1738.v1
APA Style
Mamun, A., & Dong-Ho, J. (2023). Assessment of Landslide Risk in the Jinbu Area, Korea: Integrating Spatial Models with High Resolution Social Economic Data. Preprints. https://doi.org/10.20944/preprints202311.1738.v1
Chicago/Turabian Style
Mamun, A. and Jang Dong-Ho. 2023 "Assessment of Landslide Risk in the Jinbu Area, Korea: Integrating Spatial Models with High Resolution Social Economic Data" Preprints. https://doi.org/10.20944/preprints202311.1738.v1
Abstract
This research deals with risk assessment for human life, man-made infrastructure, and agriculture in a landslide-prone area using GIS-based spatial methods. The study area landslide inventory map was prepared based on previous landslide information, aerial photograph analysis, and several field observations. A total of 550 landslides have been included with 182 debris flow and 368 soil slides. All included landslides were classified into two groups by random selection; half were used for model calibration and the rest were used for cross-validation. In the analysis, fourteen causative factors were vastly used, such as aspect, slope, curvature, elevation, topographic wetness index, forest timber diameter, forest type, forest crown density, forest age, land-use, geology, soil drainage, soil depth, and soil texture. Moreover, to identify the interaction between occurred landslides and causative factors, the affected pixels were divided into different sub-classes using a frequency ratio method. Based on the total dataset, three landslide susceptibility maps were constructed using Bayesian prediction, likelihood ratio, and fuzzy set method. By evaluating cross-validation and success rate curve, model susceptibility results were plotted with a receiver operating characteristic (ROC) curve and the area under the curve (AUC) was estimated. In addition, for risk assessment, each social data layer such as agriculture, house, industry, business, road, river, population intensity, monetary value, and vulnerability level was added based on the local standard and incident time and was converted into US dollars. During the analysis, each method hazard map was used with a specific group of thematic data layers. Subsequently, for preparing the probability table, study area total pixels and predictive landslide affected pixels were considered. Matching with the affected pixels, a standard of 5000 pixels was selected to run the final evaluation. Based on the results, the agricultural field showed the highest vulnerability and estimated risk of US $ 16.3 million. Further, the man-made infrastructure map showed a risk of US $ 31.3 million. The total estimated population casualties were 6.77, which was relatively similar to the published data.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
