Prediction of Sliding Slope Displacement Based on Intelligent Algorithm
Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zuan, Pei [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 102(2018), 4 vom: 23. Jan., Seite 3141-3157 |
|---|---|
| Übergeordnetes Werk: |
volume:102 ; year:2018 ; number:4 ; day:23 ; month:01 ; pages:3141-3157 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11277-018-5333-1 |
|---|
| Katalog-ID: |
OLC205382218X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC205382218X | ||
| 003 | DE-627 | ||
| 005 | 20230504080123.0 | ||
| 007 | tu | ||
| 008 | 200819s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11277-018-5333-1 |2 doi | |
| 035 | |a (DE-627)OLC205382218X | ||
| 035 | |a (DE-He213)s11277-018-5333-1-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 620 |q VZ |
| 100 | 1 | |a Zuan, Pei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Prediction of Sliding Slope Displacement Based on Intelligent Algorithm |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC, part of Springer Nature 2018 | ||
| 520 | |a Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. | ||
| 650 | 4 | |a Intelligent algorithm | |
| 650 | 4 | |a Slippery slope | |
| 650 | 4 | |a Displacement prediction | |
| 650 | 4 | |a RBF | |
| 650 | 4 | |a BP | |
| 700 | 1 | |a Huang, Yong |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Wireless personal communications |d Springer US, 1994 |g 102(2018), 4 vom: 23. Jan., Seite 3141-3157 |w (DE-627)188950273 |w (DE-600)1287489-9 |w (DE-576)049958909 |x 0929-6212 |7 nnns |
| 773 | 1 | 8 | |g volume:102 |g year:2018 |g number:4 |g day:23 |g month:01 |g pages:3141-3157 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11277-018-5333-1 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MKW | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 102 |j 2018 |e 4 |b 23 |c 01 |h 3141-3157 | ||
| author_variant |
p z pz y h yh |
|---|---|
| matchkey_str |
article:09296212:2018----::rdcinfldnsoeipaeetaeoi |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s11277-018-5333-1 doi (DE-627)OLC205382218X (DE-He213)s11277-018-5333-1-p DE-627 ger DE-627 rakwb eng 620 VZ Zuan, Pei verfasserin aut Prediction of Sliding Slope Displacement Based on Intelligent Algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. Intelligent algorithm Slippery slope Displacement prediction RBF BP Huang, Yong aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 4 vom: 23. Jan., Seite 3141-3157 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:4 day:23 month:01 pages:3141-3157 https://doi.org/10.1007/s11277-018-5333-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 4 23 01 3141-3157 |
| spelling |
10.1007/s11277-018-5333-1 doi (DE-627)OLC205382218X (DE-He213)s11277-018-5333-1-p DE-627 ger DE-627 rakwb eng 620 VZ Zuan, Pei verfasserin aut Prediction of Sliding Slope Displacement Based on Intelligent Algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. Intelligent algorithm Slippery slope Displacement prediction RBF BP Huang, Yong aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 4 vom: 23. Jan., Seite 3141-3157 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:4 day:23 month:01 pages:3141-3157 https://doi.org/10.1007/s11277-018-5333-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 4 23 01 3141-3157 |
| allfields_unstemmed |
10.1007/s11277-018-5333-1 doi (DE-627)OLC205382218X (DE-He213)s11277-018-5333-1-p DE-627 ger DE-627 rakwb eng 620 VZ Zuan, Pei verfasserin aut Prediction of Sliding Slope Displacement Based on Intelligent Algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. Intelligent algorithm Slippery slope Displacement prediction RBF BP Huang, Yong aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 4 vom: 23. Jan., Seite 3141-3157 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:4 day:23 month:01 pages:3141-3157 https://doi.org/10.1007/s11277-018-5333-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 4 23 01 3141-3157 |
| allfieldsGer |
10.1007/s11277-018-5333-1 doi (DE-627)OLC205382218X (DE-He213)s11277-018-5333-1-p DE-627 ger DE-627 rakwb eng 620 VZ Zuan, Pei verfasserin aut Prediction of Sliding Slope Displacement Based on Intelligent Algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. Intelligent algorithm Slippery slope Displacement prediction RBF BP Huang, Yong aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 4 vom: 23. Jan., Seite 3141-3157 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:4 day:23 month:01 pages:3141-3157 https://doi.org/10.1007/s11277-018-5333-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 4 23 01 3141-3157 |
| allfieldsSound |
10.1007/s11277-018-5333-1 doi (DE-627)OLC205382218X (DE-He213)s11277-018-5333-1-p DE-627 ger DE-627 rakwb eng 620 VZ Zuan, Pei verfasserin aut Prediction of Sliding Slope Displacement Based on Intelligent Algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. Intelligent algorithm Slippery slope Displacement prediction RBF BP Huang, Yong aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 4 vom: 23. Jan., Seite 3141-3157 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:4 day:23 month:01 pages:3141-3157 https://doi.org/10.1007/s11277-018-5333-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 4 23 01 3141-3157 |
| language |
English |
| source |
Enthalten in Wireless personal communications 102(2018), 4 vom: 23. Jan., Seite 3141-3157 volume:102 year:2018 number:4 day:23 month:01 pages:3141-3157 |
| sourceStr |
Enthalten in Wireless personal communications 102(2018), 4 vom: 23. Jan., Seite 3141-3157 volume:102 year:2018 number:4 day:23 month:01 pages:3141-3157 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Intelligent algorithm Slippery slope Displacement prediction RBF BP |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
Wireless personal communications |
| authorswithroles_txt_mv |
Zuan, Pei @@aut@@ Huang, Yong @@aut@@ |
| publishDateDaySort_date |
2018-01-23T00:00:00Z |
| hierarchy_top_id |
188950273 |
| dewey-sort |
3620 |
| id |
OLC205382218X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC205382218X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504080123.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11277-018-5333-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC205382218X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11277-018-5333-1-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zuan, Pei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of Sliding Slope Displacement Based on Intelligent Algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligent algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Slippery slope</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Displacement prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RBF</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BP</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Yong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Wireless personal communications</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">102(2018), 4 vom: 23. Jan., Seite 3141-3157</subfield><subfield code="w">(DE-627)188950273</subfield><subfield code="w">(DE-600)1287489-9</subfield><subfield code="w">(DE-576)049958909</subfield><subfield code="x">0929-6212</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:102</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">day:23</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:3141-3157</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11277-018-5333-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">102</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="b">23</subfield><subfield code="c">01</subfield><subfield code="h">3141-3157</subfield></datafield></record></collection>
|
| author |
Zuan, Pei |
| spellingShingle |
Zuan, Pei ddc 620 misc Intelligent algorithm misc Slippery slope misc Displacement prediction misc RBF misc BP Prediction of Sliding Slope Displacement Based on Intelligent Algorithm |
| authorStr |
Zuan, Pei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)188950273 |
| format |
Article |
| dewey-ones |
620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0929-6212 |
| topic_title |
620 VZ Prediction of Sliding Slope Displacement Based on Intelligent Algorithm Intelligent algorithm Slippery slope Displacement prediction RBF BP |
| topic |
ddc 620 misc Intelligent algorithm misc Slippery slope misc Displacement prediction misc RBF misc BP |
| topic_unstemmed |
ddc 620 misc Intelligent algorithm misc Slippery slope misc Displacement prediction misc RBF misc BP |
| topic_browse |
ddc 620 misc Intelligent algorithm misc Slippery slope misc Displacement prediction misc RBF misc BP |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Wireless personal communications |
| hierarchy_parent_id |
188950273 |
| dewey-tens |
620 - Engineering |
| hierarchy_top_title |
Wireless personal communications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 |
| title |
Prediction of Sliding Slope Displacement Based on Intelligent Algorithm |
| ctrlnum |
(DE-627)OLC205382218X (DE-He213)s11277-018-5333-1-p |
| title_full |
Prediction of Sliding Slope Displacement Based on Intelligent Algorithm |
| author_sort |
Zuan, Pei |
| journal |
Wireless personal communications |
| journalStr |
Wireless personal communications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
3141 |
| author_browse |
Zuan, Pei Huang, Yong |
| container_volume |
102 |
| class |
620 VZ |
| format_se |
Aufsätze |
| author-letter |
Zuan, Pei |
| doi_str_mv |
10.1007/s11277-018-5333-1 |
| dewey-full |
620 |
| title_sort |
prediction of sliding slope displacement based on intelligent algorithm |
| title_auth |
Prediction of Sliding Slope Displacement Based on Intelligent Algorithm |
| abstract |
Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 |
| container_issue |
4 |
| title_short |
Prediction of Sliding Slope Displacement Based on Intelligent Algorithm |
| url |
https://doi.org/10.1007/s11277-018-5333-1 |
| remote_bool |
false |
| author2 |
Huang, Yong |
| author2Str |
Huang, Yong |
| ppnlink |
188950273 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11277-018-5333-1 |
| up_date |
2024-07-03T20:46:59.830Z |
| _version_ |
1803592264409153536 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC205382218X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504080123.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11277-018-5333-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC205382218X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11277-018-5333-1-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zuan, Pei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of Sliding Slope Displacement Based on Intelligent Algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligent algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Slippery slope</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Displacement prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RBF</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BP</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Yong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Wireless personal communications</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">102(2018), 4 vom: 23. Jan., Seite 3141-3157</subfield><subfield code="w">(DE-627)188950273</subfield><subfield code="w">(DE-600)1287489-9</subfield><subfield code="w">(DE-576)049958909</subfield><subfield code="x">0929-6212</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:102</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">day:23</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:3141-3157</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11277-018-5333-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">102</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="b">23</subfield><subfield code="c">01</subfield><subfield code="h">3141-3157</subfield></datafield></record></collection>
|
| score |
7.3990583 |