A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor ge...

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Autores principales:	Youliang Chen, Xiangjun Zhang, Hamed Karimian, Gang Xiao, Jinsong Huang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	bat algorithm dam deformation predation kernel extreme learning machine lishan reservoir mixed kernel Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/b392041440894fc882d75cc064bd4323
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spelling	oai:doaj.org-article:b392041440894fc882d75cc064bd43232021-11-05T17:51:09ZA novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm1464-71411465-173410.2166/hydro.2021.178https://doaj.org/article/b392041440894fc882d75cc064bd43232021-09-01T00:00:00Zhttp://jh.iwaponline.com/content/23/5/935https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions. HIGHLIGHTS The Lévy flight was improved bat algorithm to overcome the shortcoming about speed and effectiveness of bat algorithm.; We integrated the advantage of Gaussian and polynomial functions as the kernel function of KELM (PGKELM).; The LBA-PGKELM at the top of LBA-SVM and BPNN which based on gradient descent method.;Youliang ChenXiangjun ZhangHamed KarimianGang XiaoJinsong HuangIWA Publishingarticlebat algorithmdam deformation predationkernel extreme learning machinelishan reservoirmixed kernelInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 5, Pp 935-949 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bat algorithm dam deformation predation kernel extreme learning machine lishan reservoir mixed kernel Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	bat algorithm dam deformation predation kernel extreme learning machine lishan reservoir mixed kernel Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Youliang Chen Xiangjun Zhang Hamed Karimian Gang Xiao Jinsong Huang A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm
description	Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions. HIGHLIGHTS The Lévy flight was improved bat algorithm to overcome the shortcoming about speed and effectiveness of bat algorithm.; We integrated the advantage of Gaussian and polynomial functions as the kernel function of KELM (PGKELM).; The LBA-PGKELM at the top of LBA-SVM and BPNN which based on gradient descent method.;
format	article
author	Youliang Chen Xiangjun Zhang Hamed Karimian Gang Xiao Jinsong Huang
author_facet	Youliang Chen Xiangjun Zhang Hamed Karimian Gang Xiao Jinsong Huang
author_sort	Youliang Chen
title	A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm
title_short	A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm
title_full	A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm
title_fullStr	A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm
title_full_unstemmed	A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm
title_sort	novel framework for prediction of dam deformation based on extreme learning machine and lévy flight bat algorithm
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/b392041440894fc882d75cc064bd4323
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A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

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