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
Autor*in: |
Jiang, Ping [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Schlagwörter: |
Pearson cross-correlation coefficients |
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Umfang: |
8 |
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Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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Übergeordnetes Werk: |
volume:198 ; year:2016 ; day:19 ; month:07 ; pages:40-47 ; extent:8 |
Links: |
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DOI / URN: |
10.1016/j.neucom.2015.08.118 |
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ELV024599409 |
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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 |
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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 |
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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 |
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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 |
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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 |
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Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation |
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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. |
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