UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A Novel Method for Landslide Displacement Prediction by Integrating Advanced Computational Intelligence Algorithms

Zhou, C; Yin, K; Ahmed, B; Fu, X; (2018) A Novel Method for Landslide Displacement Prediction by Integrating Advanced Computational Intelligence Algorithms. Scientific Reports , 8 (7287) 10.1038/s41598-018-25567-6. Green open access

[thumbnail of NSR_Bayes.pdf]
Preview
Text
NSR_Bayes.pdf - Published Version

Download (5MB) | Preview

Abstract

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.

Type: Article
Title: A Novel Method for Landslide Displacement Prediction by Integrating Advanced Computational Intelligence Algorithms
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41598-018-25567-6
Publisher version: http://doi.org/10.1038/s41598-018-25567-6
Language: English
Additional information: Copyright © The Author(s) 2018 Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Landslides, China
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction
URI: https://discovery.ucl.ac.uk/id/eprint/10048260
Downloads since deposit
75Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item