Next Article in Journal
Validation of Stratification-Driven Phytoplankton Biomass and Nutrient Concentrations in the Northeast Atlantic Ocean as Simulated by EC-Earth
Previous Article in Journal
Application of EPS Geofoam to a Soil–Steel Bridge to Reduce Seismic Excitations
Open AccessArticle

Quantitative Hazard Assessment of Landslides Using the Levenburg–Marquardt Back Propagation Neural Network Method in a Pipeline Area

by Junnan Xiong 1,2, Jin Li 1, Hao Zhang 1,3,*, Ming Sun 4 and Weiming Cheng 2,5
1
School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Institute of Mountain Disasters and Environment, Chinese Academy of Sciences, Chengdu 610041, China
4
The First Surveying and Mapping Engineering Institute of Sichuan Province, Chengdu 610100, China
5
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(10), 449; https://doi.org/10.3390/geosciences9100449
Received: 18 September 2019 / Revised: 17 October 2019 / Accepted: 18 October 2019 / Published: 21 October 2019
Pipelines are exposed to the severe threat of natural disasters, where the damage caused by landslides are particularly bad. Hence, in the route arrangement and maintenance management of pipeline projects, it is particularly important to evaluate the regional landslide hazards in advance. However, most models are based on the subjective determination of evaluation factors and index weights; this study establishes a quantitative hazard assessment model based on the location of historical landslides and the Levenberg–Marquardt Back Propagation (LM-BP) Neural Network model was applied to the pipeline area. We established an evaluation index system by analyzing the spatial patterns of single assessment factors and the mechanism of landslides. Then, different from previous studies, we built the standard sample matrix of the LM-BP neural network by using interpolation theory to avoid the serious influence of human factors on the hazard assessment. Finally, we used the standard sample matrix and the historical data to learn, train, test, and simulate future results. Our results showed 33 slopes with low hazard (accounting for 10.48% of the total number of slopes and corresponding to approximately 32.63 km2), 62 slopes with moderate hazard (accounting for 19.68% of the total number of slopes and corresponding to approximately 65.53 km2), 112 slopes with high hazard (accounting for 35.56% of the total number of slopes and corresponding to approximately 123.55 km2), and 108 slopes with extremely high hazard (accounting for 34.29% of the total number of slopes and corresponding to approximately 150.65 km2). Local spatial autocorrelation analysis indicated that there are significant “high–high” and “low–low” aggregation of landslide hazards in the pipeline area. By comparing the model results with the past landslides, new landslides and landslide potential points, its prediction capability and accuracy were confirmed. On the basis of the results, our study has developed effective risk prevention and mitigation strategies in mountain areas to promote pipeline safety. View Full-Text
Keywords: landslide; hazard assessment; Levenberg–Marquardt Back Propagation (LM-BP) neural network; pipeline area landslide; hazard assessment; Levenberg–Marquardt Back Propagation (LM-BP) neural network; pipeline area
Show Figures

Figure 1

MDPI and ACS Style

Xiong, J.; Li, J.; Zhang, H.; Sun, M.; Cheng, W. Quantitative Hazard Assessment of Landslides Using the Levenburg–Marquardt Back Propagation Neural Network Method in a Pipeline Area. Geosciences 2019, 9, 449.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop