Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm

Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm

To find suitable for small water conservancy engineering standard method for prediction of subsidence. This paper based on the genetic algorithm GA optimization extreme learning machine, three different ELM model activation function. From this, six computational models are obtained. According to the...

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Main Author: Yang Ruizi
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/24/e3sconf_caes2021_03065.pdf
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spelling doaj-37a7fb8eda8b4a838c01a5029293e3592021-04-13T09:03:02ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012480306510.1051/e3sconf/202124803065e3sconf_caes2021_03065Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic AlgorithmYang Ruizi0Management Science and Engineering, School of management, Tianjin University of TechnologyTo find suitable for small water conservancy engineering standard method for prediction of subsidence. This paper based on the genetic algorithm GA optimization extreme learning machine, three different ELM model activation function. From this, six computational models are obtained. According to the input of groundwater dynamic changes, precipitation, temperature and soil four indicators of the two kinds of input combinations, a total of 12 kinds of model input. It’s concluded that the optimal settlement prediction model, the results showed that: Ga-ELMsin model shows high accuracy, and genetic algorithm can improve the calculation accuracy of ELM model. Groundwater dynamics is the main factor affecting settlement.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/24/e3sconf_caes2021_03065.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Yang Ruizi
spellingShingle Yang Ruizi
Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm
E3S Web of Conferences
author_facet Yang Ruizi
author_sort Yang Ruizi
title Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm
title_short Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm
title_full Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm
title_fullStr Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm
title_full_unstemmed Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm
title_sort research on settlement prediction of small water conservancy project based on elm model optimized by genetic algorithm
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2021-01-01
description To find suitable for small water conservancy engineering standard method for prediction of subsidence. This paper based on the genetic algorithm GA optimization extreme learning machine, three different ELM model activation function. From this, six computational models are obtained. According to the input of groundwater dynamic changes, precipitation, temperature and soil four indicators of the two kinds of input combinations, a total of 12 kinds of model input. It’s concluded that the optimal settlement prediction model, the results showed that: Ga-ELMsin model shows high accuracy, and genetic algorithm can improve the calculation accuracy of ELM model. Groundwater dynamics is the main factor affecting settlement.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/24/e3sconf_caes2021_03065.pdf
work_keys_str_mv AT yangruizi researchonsettlementpredictionofsmallwaterconservancyprojectbasedonelmmodeloptimizedbygeneticalgorithm
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