A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms
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...
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doaj-987cf67a18e648a09da92652c2a879992020-12-08T03:50:21ZengNature Publishing GroupScientific Reports2045-23222018-05-018111210.1038/s41598-018-25567-6A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithmsChao Zhou0Kunlong Yin1Ying Cao2Bayes Ahmed3Xiaolin Fu4Engineering Faculty, China University of GeosciencesEngineering Faculty, China University of GeosciencesEngineering Faculty, China University of GeosciencesInstitute for Risk and Disaster Reduction, University College London (UCL)Administration of Prevention and Control of GeoHazards in the Three Gorges Reservoir of ChinaAbstract 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.https://doi.org/10.1038/s41598-018-25567-6 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chao Zhou Kunlong Yin Ying Cao Bayes Ahmed Xiaolin Fu |
spellingShingle |
Chao Zhou Kunlong Yin Ying Cao Bayes Ahmed Xiaolin Fu A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms Scientific Reports |
author_facet |
Chao Zhou Kunlong Yin Ying Cao Bayes Ahmed Xiaolin Fu |
author_sort |
Chao Zhou |
title |
A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms |
title_short |
A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms |
title_full |
A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms |
title_fullStr |
A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms |
title_full_unstemmed |
A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms |
title_sort |
novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2018-05-01 |
description |
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. |
url |
https://doi.org/10.1038/s41598-018-25567-6 |
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