Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation
In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-corre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Ping [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
|---|
| Schlagwörter: |
Pearson cross-correlation coefficients |
|---|
| Umfang: |
8 |
|---|
| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:198 ; year:2016 ; day:19 ; month:07 ; pages:40-47 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2015.08.118 |
|---|
| Katalog-ID: |
ELV024599409 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV024599409 | ||
| 003 | DE-627 | ||
| 005 | 20230623174339.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2016 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.neucom.2015.08.118 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000157A.pica |
| 035 | |a (DE-627)ELV024599409 | ||
| 035 | |a (ELSEVIER)S0925-2312(16)00313-1 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 610 | |
| 082 | 0 | 4 | |a 610 |q DE-600 |
| 082 | 0 | 4 | |a 570 |q VZ |
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 35.70 |2 bkl | ||
| 084 | |a 42.12 |2 bkl | ||
| 100 | 1 | |a Jiang, Ping |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation |
| 264 | 1 | |c 2016 | |
| 300 | |a 8 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. | ||
| 650 | 7 | |a Landslide |2 Elsevier | |
| 650 | 7 | |a Pearson cross-correlation coefficients |2 Elsevier | |
| 650 | 7 | |a Generalized regression neural networks |2 Elsevier | |
| 650 | 7 | |a Mutual information |2 Elsevier | |
| 700 | 1 | |a Chen, Jiejie |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
| 773 | 1 | 8 | |g volume:198 |g year:2016 |g day:19 |g month:07 |g pages:40-47 |g extent:8 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.neucom.2015.08.118 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 35.70 |j Biochemie: Allgemeines |q VZ |
| 936 | b | k | |a 42.12 |j Biophysik |q VZ |
| 951 | |a AR | ||
| 952 | |d 198 |j 2016 |b 19 |c 0719 |h 40-47 |g 8 | ||
| 953 | |2 045F |a 610 | ||
| author_variant |
p j pj |
|---|---|
| matchkey_str |
jiangpingchenjiejie:2016----:ipaeetrdcinfadldbsdneeaiergesonuanto |
| hierarchy_sort_str |
2016 |
| bklnumber |
35.70 42.12 |
| publishDate |
2016 |
| allfields |
10.1016/j.neucom.2015.08.118 doi GBV00000000000157A.pica (DE-627)ELV024599409 (ELSEVIER)S0925-2312(16)00313-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Jiang, Ping verfasserin aut Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation 2016 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information Elsevier Chen, Jiejie oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:198 year:2016 day:19 month:07 pages:40-47 extent:8 https://doi.org/10.1016/j.neucom.2015.08.118 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 198 2016 19 0719 40-47 8 045F 610 |
| spelling |
10.1016/j.neucom.2015.08.118 doi GBV00000000000157A.pica (DE-627)ELV024599409 (ELSEVIER)S0925-2312(16)00313-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Jiang, Ping verfasserin aut Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation 2016 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information Elsevier Chen, Jiejie oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:198 year:2016 day:19 month:07 pages:40-47 extent:8 https://doi.org/10.1016/j.neucom.2015.08.118 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 198 2016 19 0719 40-47 8 045F 610 |
| allfields_unstemmed |
10.1016/j.neucom.2015.08.118 doi GBV00000000000157A.pica (DE-627)ELV024599409 (ELSEVIER)S0925-2312(16)00313-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Jiang, Ping verfasserin aut Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation 2016 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information Elsevier Chen, Jiejie oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:198 year:2016 day:19 month:07 pages:40-47 extent:8 https://doi.org/10.1016/j.neucom.2015.08.118 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 198 2016 19 0719 40-47 8 045F 610 |
| allfieldsGer |
10.1016/j.neucom.2015.08.118 doi GBV00000000000157A.pica (DE-627)ELV024599409 (ELSEVIER)S0925-2312(16)00313-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Jiang, Ping verfasserin aut Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation 2016 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information Elsevier Chen, Jiejie oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:198 year:2016 day:19 month:07 pages:40-47 extent:8 https://doi.org/10.1016/j.neucom.2015.08.118 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 198 2016 19 0719 40-47 8 045F 610 |
| allfieldsSound |
10.1016/j.neucom.2015.08.118 doi GBV00000000000157A.pica (DE-627)ELV024599409 (ELSEVIER)S0925-2312(16)00313-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Jiang, Ping verfasserin aut Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation 2016 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information Elsevier Chen, Jiejie oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:198 year:2016 day:19 month:07 pages:40-47 extent:8 https://doi.org/10.1016/j.neucom.2015.08.118 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 198 2016 19 0719 40-47 8 045F 610 |
| language |
English |
| source |
Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:198 year:2016 day:19 month:07 pages:40-47 extent:8 |
| sourceStr |
Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:198 year:2016 day:19 month:07 pages:40-47 extent:8 |
| format_phy_str_mv |
Article |
| bklname |
Biochemie: Allgemeines Biophysik |
| institution |
findex.gbv.de |
| topic_facet |
Landslide Pearson cross-correlation coefficients Generalized regression neural networks Mutual information |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| authorswithroles_txt_mv |
Jiang, Ping @@aut@@ Chen, Jiejie @@oth@@ |
| publishDateDaySort_date |
2016-01-19T00:00:00Z |
| hierarchy_top_id |
ELV002603926 |
| dewey-sort |
3610 |
| id |
ELV024599409 |
| 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">ELV024599409</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230623174339.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2015.08.118</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000157A.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV024599409</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(16)00313-1</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=" "><subfield code="a">610</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Ping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">8</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Landslide</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Pearson cross-correlation coefficients</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Generalized regression neural networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Mutual information</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Jiejie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:198</subfield><subfield code="g">year:2016</subfield><subfield code="g">day:19</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:40-47</subfield><subfield code="g">extent:8</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2015.08.118</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">198</subfield><subfield code="j">2016</subfield><subfield code="b">19</subfield><subfield code="c">0719</subfield><subfield code="h">40-47</subfield><subfield code="g">8</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">610</subfield></datafield></record></collection>
|
| author |
Jiang, Ping |
| spellingShingle |
Jiang, Ping ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation |
| authorStr |
Jiang, Ping |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV002603926 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health 570 - Life sciences; biology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information Elsevier |
| topic |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information |
| topic_unstemmed |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information |
| topic_browse |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Landslide Elsevier Pearson cross-correlation coefficients Elsevier Generalized regression neural networks Elsevier Mutual information |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
j c jc |
| hierarchy_parent_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| hierarchy_parent_id |
ELV002603926 |
| dewey-tens |
610 - Medicine & health 570 - Life sciences; biology |
| hierarchy_top_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV002603926 |
| title |
Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation |
| ctrlnum |
(DE-627)ELV024599409 (ELSEVIER)S0925-2312(16)00313-1 |
| title_full |
Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation |
| author_sort |
Jiang, Ping |
| journal |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| journalStr |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 500 - Science |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
zzz |
| container_start_page |
40 |
| author_browse |
Jiang, Ping |
| container_volume |
198 |
| physical |
8 |
| class |
610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Jiang, Ping |
| doi_str_mv |
10.1016/j.neucom.2015.08.118 |
| dewey-full |
610 570 |
| title_sort |
displacement prediction of landslide based on generalized regression neural networks with k-fold cross-validation |
| title_auth |
Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation |
| abstract |
In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. |
| abstractGer |
In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. |
| abstract_unstemmed |
In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
| title_short |
Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation |
| url |
https://doi.org/10.1016/j.neucom.2015.08.118 |
| remote_bool |
true |
| author2 |
Chen, Jiejie |
| author2Str |
Chen, Jiejie |
| ppnlink |
ELV002603926 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.neucom.2015.08.118 |
| up_date |
2024-07-06T21:51:30.688Z |
| _version_ |
1803868114191908864 |
| 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">ELV024599409</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230623174339.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2015.08.118</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000157A.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV024599409</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(16)00313-1</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=" "><subfield code="a">610</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Ping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">8</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Landslide</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Pearson cross-correlation coefficients</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Generalized regression neural networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Mutual information</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Jiejie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:198</subfield><subfield code="g">year:2016</subfield><subfield code="g">day:19</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:40-47</subfield><subfield code="g">extent:8</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2015.08.118</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">198</subfield><subfield code="j">2016</subfield><subfield code="b">19</subfield><subfield code="c">0719</subfield><subfield code="h">40-47</subfield><subfield code="g">8</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">610</subfield></datafield></record></collection>
|
| score |
7.3995304 |