Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
04 Mar 2013
Review status
This discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The revised manuscript was not accepted.
Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility
H. B. Wang1,2, J. W. Li1, B. Zhou1, Z. Q. Yuan1, and Y. P. Chen3 1Institute of Geotechnical and Underground Engineering, Huazhong University of Science & Technology, Wuhan 430074, P. R. China
2Hubei Key Laboratory of Control Structure, Huazhong University of Science & Technology, Wuhan 430074, P. R. China
3Wenhua College, Huazhong University of Science & Technology, Wuhan 430074, P. R. China
Abstract. In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100–150 m with slope angles from 135°–225° and 40°–60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence is 93.02%, whereas units without landslide occurrence are predicted with an accuracy of 81.13%. To sum up, the verification shows satisfactory agreement with an accuracy of 86.46% between the susceptibility map and the landslide locations. In the landslide susceptibility assessment, ten new slopes were predicted to show potential for failure, which can be confirmed by the engineering geological conditions of these slopes. It was also observed that some disadvantages could be overcome in the application of the neural networks with back propagation, for example, the low convergence rate and local minimum, after the network was optimized using genetic algorithms. To conclude, neural networks with back propagation that are optimized by genetic algorithms are an effective method to predict landslide susceptibility with high accuracy.

Citation: Wang, H. B., Li, J. W., Zhou, B., Yuan, Z. Q., and Chen, Y. P.: Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility, Nat. Hazards Earth Syst. Sci. Discuss.,, 2013.
H. B. Wang et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
RC C54: 'neural networks, genetic algorithms, landslide susceptibility', Anonymous Referee #1, 28 Mar 2013 Printer-friendly Version 
AC C141: 'Response', Huabin Wang, 23 Apr 2013 Printer-friendly Version Supplement 
RC C91: 'Review_nhessd-2013-37', Anonymous Referee #2, 12 Apr 2013 Printer-friendly Version 
AC C144: 'Response', Huabin Wang, 23 Apr 2013 Printer-friendly Version Supplement 
H. B. Wang et al.
H. B. Wang et al.


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