Landslide Displacement Prediction Method Based on GA-Elman Model

The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displ...

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Autores principales:	Chenhui Wang, Yijiu Zhao, Libing Bai, Wei Guo, Qingjia Meng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	landslide displacement prediction model genetic algorithm Elman neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/cc6ba143f4e74f70a903fb4e9b6ff4c3
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id	oai:doaj.org-article:cc6ba143f4e74f70a903fb4e9b6ff4c3
record_format	dspace
spelling	oai:doaj.org-article:cc6ba143f4e74f70a903fb4e9b6ff4c32021-11-25T16:43:07ZLandslide Displacement Prediction Method Based on GA-Elman Model10.3390/app1122110302076-3417https://doaj.org/article/cc6ba143f4e74f70a903fb4e9b6ff4c32021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11030https://doaj.org/toc/2076-3417The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.Chenhui WangYijiu ZhaoLibing BaiWei GuoQingjia MengMDPI AGarticlelandslide displacementprediction modelgenetic algorithmElman neural networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11030, p 11030 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landslide displacement prediction model genetic algorithm Elman neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	landslide displacement prediction model genetic algorithm Elman neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Chenhui Wang Yijiu Zhao Libing Bai Wei Guo Qingjia Meng Landslide Displacement Prediction Method Based on GA-Elman Model
description	The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.
format	article
author	Chenhui Wang Yijiu Zhao Libing Bai Wei Guo Qingjia Meng
author_facet	Chenhui Wang Yijiu Zhao Libing Bai Wei Guo Qingjia Meng
author_sort	Chenhui Wang
title	Landslide Displacement Prediction Method Based on GA-Elman Model
title_short	Landslide Displacement Prediction Method Based on GA-Elman Model
title_full	Landslide Displacement Prediction Method Based on GA-Elman Model
title_fullStr	Landslide Displacement Prediction Method Based on GA-Elman Model
title_full_unstemmed	Landslide Displacement Prediction Method Based on GA-Elman Model
title_sort	landslide displacement prediction method based on ga-elman model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cc6ba143f4e74f70a903fb4e9b6ff4c3
work_keys_str_mv	AT chenhuiwang landslidedisplacementpredictionmethodbasedongaelmanmodel AT yijiuzhao landslidedisplacementpredictionmethodbasedongaelmanmodel AT libingbai landslidedisplacementpredictionmethodbasedongaelmanmodel AT weiguo landslidedisplacementpredictionmethodbasedongaelmanmodel AT qingjiameng landslidedisplacementpredictionmethodbasedongaelmanmodel
_version_	1718413043598098432

Landslide Displacement Prediction Method Based on GA-Elman Model

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