A Deformation Forecasting Model of High and Steep Slope Based on Fuzzy Time Series and Entire Distribution Optimization
Because the deformation of the slope is affected by the stability of the underground structure, natural factors, and human factors, it is difficult for the traditional prediction model of the slope to accurately predict sudden changes. This paper proposes a method to predict the deformation of high...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.176112-176121 |
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| creator | Fu, Yanhua Wan, Lushan Fu, Xiaorui Xiao, Dong Mao, Yachun Sun, Xiaoyu |
| description | Because the deformation of the slope is affected by the stability of the underground structure, natural factors, and human factors, it is difficult for the traditional prediction model of the slope to accurately predict sudden changes. This paper proposes a method to predict the deformation of high and steep slopes based on the fuzzy time series and Entire Distribution Optimization. The division of the domain is optimized by the Entire Distribution Optimization, and the deformation of high and steep slopes is predicted by the fuzzy time series. The experimental results show that the fuzzy time series has a good predictive effect on the number of mutations, and the Entire Distribution Optimization avoids the one-sidedness of dividing the domain by mean, which improves the accuracy of the deformation forecasting model of the high and steep slope. |
| doi_str_mv | 10.1109/ACCESS.2020.3027206 |
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This paper proposes a method to predict the deformation of high and steep slopes based on the fuzzy time series and Entire Distribution Optimization. The division of the domain is optimized by the Entire Distribution Optimization, and the deformation of high and steep slopes is predicted by the fuzzy time series. 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(IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>2</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000575084200001</woscitedreferencesoriginalsourcerecordid><cites>FETCH-LOGICAL-c358t-6aaf0d5ba5df5a65adb4b99d41b1192ed2b0bc1bd80b75945176bb004bc13b023</cites><orcidid>0000-0003-2589-8593 ; 0000-0002-0401-6654</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9207755$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,865,2103,2115,4025,27637,27927,27928,27929,54937</link.rule.ids></links><search><creatorcontrib>Fu, Yanhua</creatorcontrib><creatorcontrib>Wan, Lushan</creatorcontrib><creatorcontrib>Fu, Xiaorui</creatorcontrib><creatorcontrib>Xiao, Dong</creatorcontrib><creatorcontrib>Mao, Yachun</creatorcontrib><creatorcontrib>Sun, Xiaoyu</creatorcontrib><title>A Deformation Forecasting Model of High and Steep Slope Based on Fuzzy Time Series and Entire Distribution Optimization</title><title>IEEE access</title><addtitle>Access</addtitle><addtitle>IEEE ACCESS</addtitle><description>Because the deformation of the slope is affected by the stability of the underground structure, natural factors, and human factors, it is difficult for the traditional prediction model of the slope to accurately predict sudden changes. 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The experimental results show that the fuzzy time series has a good predictive effect on the number of mutations, and the Entire Distribution Optimization avoids the one-sidedness of dividing the domain by mean, which improves the accuracy of the deformation forecasting model of the high and steep slope.</description><subject>Azimuth</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Deformation</subject><subject>Deformation of the slope</subject><subject>Domains</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>entire distribution optimization</subject><subject>Forecasting</subject><subject>fuzzy time series</subject><subject>Human factors</subject><subject>Mathematical models</subject><subject>mine</subject><subject>Model accuracy</subject><subject>Mutation</subject><subject>Optimization</subject><subject>Prediction models</subject><subject>Predictive models</subject><subject>Science & Technology</subject><subject>Slope stability</subject><subject>Sociology</subject><subject>Strain</subject><subject>Structural stability</subject><subject>Technology</subject><subject>Telecommunications</subject><subject>Time series</subject><subject>Time series analysis</subject><subject>Underground structures</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkUFv3CAUhK2qlRql-QW5IPVY7RYwGPu4dTZNpFQ5OD0jMI8tq13jAlaU_fVl7SjtsVxAo5l5T3xFcU3wmhDcfN207bbr1hRTvC4xFRRX74oLSqpmVfKyev_P-2NxFeMe51NniYuL4nmDbsD6cFTJ-QHd-gC9iskNO_TDGzggb9Gd2_1CajCoSwAj6g5-BPRNRTDoHJlOpxf05I6AOggO4mzdDskFQDcupuD0NJc_jskd3Wme9Kn4YNUhwtXrfVn8vN0-tXerh8fv9-3mYdWXvE6rSimLDdeKG8tVxZXRTDeNYUQT0lAwVGPdE21qrAVvGCei0hpjlsVSY1peFvdLr_FqL8fgjiq8SK-cnAUfdlKF5PoDSCWELmvKrFCM9dpqRoUQlSDAuaWE5K7PS9cY_O8JYpJ7P4Uhry8p46yqMREiu8rF1QcfYwD7NpVgeQYmF2DyDEy-Asupekk9g_Y29g6GHt6SGRgXHNeMntmR1qX5E1s_DSlHv_x_NLuvF7cD-OtqKBaC8_IP6QGyIA</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Fu, Yanhua</creator><creator>Wan, Lushan</creator><creator>Fu, Xiaorui</creator><creator>Xiao, Dong</creator><creator>Mao, Yachun</creator><creator>Sun, Xiaoyu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This paper proposes a method to predict the deformation of high and steep slopes based on the fuzzy time series and Entire Distribution Optimization. The division of the domain is optimized by the Entire Distribution Optimization, and the deformation of high and steep slopes is predicted by the fuzzy time series. The experimental results show that the fuzzy time series has a good predictive effect on the number of mutations, and the Entire Distribution Optimization avoids the one-sidedness of dividing the domain by mean, which improves the accuracy of the deformation forecasting model of the high and steep slope.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3027206</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-2589-8593</orcidid><orcidid>https://orcid.org/0000-0002-0401-6654</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Azimuth Computer Science Computer Science, Information Systems Deformation Deformation of the slope Domains Engineering Engineering, Electrical & Electronic entire distribution optimization Forecasting fuzzy time series Human factors Mathematical models mine Model accuracy Mutation Optimization Prediction models Predictive models Science & Technology Slope stability Sociology Strain Structural stability Technology Telecommunications Time series Time series analysis Underground structures |
| title | A Deformation Forecasting Model of High and Steep Slope Based on Fuzzy Time Series and Entire Distribution Optimization |
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