Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction
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ORIGINAL PAPER
Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction Yong-gang Zhang1,2,3 • Jun Tang4,5 • Rao-ping Liao1 Zheng-yang Su9
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Ming-fei Zhang6 • Yan Zhang7 • Xiao-ming Wang8
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Accepted: 23 October 2020 Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The landslide caused a huge disaster to the living environment and seriously threatened the lives and property safety of nearby residents. Assess or predict landslide-susceptible the landslide displacement through monitoring are great beneficial to guide landslide control and mitigate these hazards by taking appropriate preparatory measures. In this paper, a new water cycle algorithm optimization BP neural network (BPNN) dynamic prediction model (WCA-BPNN) was established to make up for the shortcoming of BPNN convergence speed. A typical step-wise landslide——Langshuwan Landslide happened in the Three Gorges Reservoir area of China is taken as a case, and the displacement monitoring data of 4 years was used for time series analysis and modeling. The long-term creep effect of the landslide and the short-term acceleration effect of the climate are considered in the model, and the accumulative displacement is divided into two kinds of trend displacement and periodic displacement. The key influencing factors of landslide periodic displacement were screened by gray relational grade analysis method, and then used as learning data. In addition to comparing the predicted value of the model with the measured value, it also compares the accuracy of the three models of BPNN, support vector machine, extreme learning machine under the training conditions of the same learning data set. The results show that the WACBPNN model has faster convergence speed and higher prediction accuracy than the traditional BPNN model, and it is also the most accurate of the four models. Keywords Multifactor-induced landslide Displacement prediction Water cycle algorithm BP neural network Dynamic prediction model
& Rao-ping Liao [email protected] 1
Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, and Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China
2
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221000, China
3
China Geological Survey, Beijing 100037, China
4
Xiamen Xijiao Hard Science Industrial Technology Research Institute Co., Ltd, Xiamen 316000, China
5
College of Civil Engineering, Huaqiao University, Xiamen 316000, China
6
Civil Engineering and Architecture Institute, Zhengzhou University of Aeronautics, Zhengzhou 450046, Henan, China
7
College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
8
School of Civil Engineering, Central South University, Changsha 410083, China
9
Nanjing Hydraulic Research Institute, Nanjing 210029, China
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Stochastic Environmental Research and Risk Assessment
1 Introduction There is no doubt that landslides
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