|Table of Contents|

High-precision Landslide Displacement Prediction Model Based on IHHO Algorithm Combined with BP Neural Network(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2023年第03期
Page:
522-534
Research Field:
大地测量、遥感与地学大数据
Publishing date:

Info

Title:
High-precision Landslide Displacement Prediction Model Based on IHHO Algorithm Combined with BP Neural Network
Author(s):
QU Wei;  LIU Xiang-bin;  LI Jiu-yuan;  WANG Yu-hao;  LI Da
(School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China)
Keywords:
loess landslide;  displacement predication;  IHHO algorithm;  BP neural network;  Levy variation;  local enhancement;  randomized Halton sequence;  Heifangtai
PACS:
P228; P642.22
DOI:
10.19814/j.jese.2022.11062
Abstract:
High-precision prediction of landslide displacement has important reference value for landslide disaster prevention and early warning. Aiming at the problem that the Harris Hawks optimization(HHO)algorithm has low search accuracy and falls into local optimum, while effectively taking into account the external influence factors of landslides, a high-precision landslide displacement prediction model was established based on improved Harris Hawks optimization(IHHO)algorithm combined with BP neural network(IHHO-BP). Combined with the measured Beidou/GNSS data(HF08, HF05 and HF09 monitoring points)of the Dangchuan landslide in Heifangtai area of Gansu, which is a typical loess landslide in China, the displacement prediction accuracy of IHHO-BP model is verified to be better than that of the single BP neural network, and the combination prediction models of HHO, sparrow search algorithm(SSA), particle swarm optimization(PSO), genetic algorithm(GA)and BP neural network. The results show that IHHO algorithm, which introduces Levy variation, local enhancement and randomized Halton sequence population initialization strategy, can effectively solve the problem that HHO algorithm has low search accuracy and falls into local optimization; the IHHO-BP model has better generalization ability and can effectively improve the prediction accuracy of landslide displacement, which is also has better popularization and application value.

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Last Update: 2023-05-30