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Open AccessArticle

Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China

1
School of Environment, Northeast Normal University, Changchun 130024, China
2
Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130117, China
3
State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun 130117, China
4
Jilin Institute of Geological Environment Monitoring, Changchun 130061, China
5
Changchun Institute of Technology, Changchun 130021, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(10), 2144; https://doi.org/10.3390/w11102144
Received: 9 September 2019 / Revised: 11 October 2019 / Accepted: 14 October 2019 / Published: 15 October 2019
(This article belongs to the Special Issue The Artificial Intelligence Models for Landslide Hazard Assessment)
The aim of this project was to produce an earthquake–landslide debris flow disaster chain susceptibility map for the Changbai Mountain region, China, by applying data-driven model series and parallel model and Bayesian Networks model. The accuracy of these two models was then compared. Parameters related to the occurrence of landslide and debris flow disasters, including earthquake intensity, rainfall, elevation, slope, slope aspect, lithology, distance to rivers, distance to faults, land use, and the normalized difference vegetation index (NDVI), were chosen and applied in these two models. Disaster chain susceptibility zones created using the two models were then contrasted and verified using the occurrence of past disasters obtained from remote sensing interpretations and field investigations. Both disaster chain susceptibility maps showed that the high susceptibility zones are situated within a 10 km radius around the Tianchi volcano, whereas the northern and southwestern sections of the study area comprise primarily very low or low susceptibility zones. The two models produced similar and compatible results as indicated by the outcomes of basic linear correlation and cross-correlation analyses. The verification results of the ROC curves were found to be 0.7727 and 0.8062 for the series and parallel model and BN model, respectively. These results indicate that the two models can be used as a preliminary base for further research activities aimed at providing hazard management tools, forecasting services, and early warning systems. View Full-Text
Keywords: landslide; debris flow; series and parallel model; Bayesian Networks model; susceptibility; Changbai Mountain landslide; debris flow; series and parallel model; Bayesian Networks model; susceptibility; Changbai Mountain
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Han, L.; Zhang, J.; Zhang, Y.; Lang, Q. Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China. Water 2019, 11, 2144.

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