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NHESS | Articles | Volume 19, issue 3
Nat. Hazards Earth Syst. Sci., 19, 629–653, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Advances in computational modelling of natural hazards and...

Nat. Hazards Earth Syst. Sci., 19, 629–653, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Mar 2019

Research article | 25 Mar 2019

Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Junnan Xiong et al.
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Cited articles  
Akgun, A., Kıncal, C., and Pradhan, B.: Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey), Environ. Monit. Assess., 184, 5453–5470,, 2012. 
Atta-Ur-Rahman and Shaw, R.: Hazard, Vulnerability and Risk: The Pakistan Context, Springer, Japan, 2015. 
Avalon Cullen, C., Al-Suhili, R., and Khanbilvardi, R.: Guidance Index for Shallow Landslide Hazard Analysis, Remote. Sens., 8, 866,, 2016. 
Chang, H. and Kim, N. K.: The evaluation and the sensitivity analysis of GIS-based landslide susceptibility models, Geosci. J., 8, 415–423,, 2004. 
Ding, M. and Tian, S.: Landslide and Debris Flow Risk Assessment and Its Application, Science Press, Beijing, 2013. 
Publications Copernicus
Short summary
We want to know which areas are prone to landslides and where pipelines are more unsafe. Through a model, we determined that 33.18 % and 40.46 % of the slopes in the study are were in high-hazard and extremely high-hazard areas, respectively. The number and length of pipe segments in the highly vulnerable and extremely vulnerable areas accounted for about 12 % of the total. In general, the pipeline risk within Qingchuan and Jian'ge counties was relatively high.
We want to know which areas are prone to landslides and where pipelines are more unsafe. Through...