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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Autores principales:	Chao Zhou, Kunlong Yin, Ying Cao, Bayes Ahmed, Xiaolin Fu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/987cf67a18e648a09da92652c2a87999
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spelling	oai:doaj.org-article:987cf67a18e648a09da92652c2a879992021-12-02T16:08:24ZA novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms10.1038/s41598-018-25567-62045-2322https://doaj.org/article/987cf67a18e648a09da92652c2a879992018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25567-6https://doaj.org/toc/2045-2322Abstract 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.Chao ZhouKunlong YinYing CaoBayes AhmedXiaolin FuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chao Zhou Kunlong Yin Ying Cao Bayes Ahmed Xiaolin Fu A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms
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.
format	article
author	Chao Zhou Kunlong Yin Ying Cao Bayes Ahmed Xiaolin Fu
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 Portfolio
publishDate	2018
url	https://doaj.org/article/987cf67a18e648a09da92652c2a87999
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A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

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