Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation
• A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realiza...
Ausführliche Beschreibung
Autor*in: |
Chen, Qiyu [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Neighborhood resources associated with frailty trajectories over time among community-dwelling older adults in China - Liu, Huiying ELSEVIER, 2021, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:610 ; year:2022 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.jhydrol.2022.127970 |
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10.1016/j.jhydrol.2022.127970 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001936.pica (DE-627)ELV058198911 (ELSEVIER)S0022-1694(22)00545-5 DE-627 ger DE-627 rakwb eng 610 VZ 74.00 bkl 44.73 bkl Chen, Qiyu verfasserin aut Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. Deep learning Elsevier Hydrological structures Elsevier Generative adversarial networks Elsevier Heterogeneous patterns Elsevier Monte-Carlo simulation Elsevier Cui, Zhesi oth Liu, Gang oth Yang, Zixiao oth Ma, Xiaogang oth Enthalten in Elsevier Liu, Huiying ELSEVIER Neighborhood resources associated with frailty trajectories over time among community-dwelling older adults in China 2021 Amsterdam [u.a.] (DE-627)ELV007566492 volume:610 year:2022 pages:0 https://doi.org/10.1016/j.jhydrol.2022.127970 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 74.00 Geographie Anthropogeographie: Allgemeines VZ 44.73 Geomedizin VZ AR 610 2022 0 |
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10.1016/j.jhydrol.2022.127970 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001936.pica (DE-627)ELV058198911 (ELSEVIER)S0022-1694(22)00545-5 DE-627 ger DE-627 rakwb eng 610 VZ 74.00 bkl 44.73 bkl Chen, Qiyu verfasserin aut Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. Deep learning Elsevier Hydrological structures Elsevier Generative adversarial networks Elsevier Heterogeneous patterns Elsevier Monte-Carlo simulation Elsevier Cui, Zhesi oth Liu, Gang oth Yang, Zixiao oth Ma, Xiaogang oth Enthalten in Elsevier Liu, Huiying ELSEVIER Neighborhood resources associated with frailty trajectories over time among community-dwelling older adults in China 2021 Amsterdam [u.a.] (DE-627)ELV007566492 volume:610 year:2022 pages:0 https://doi.org/10.1016/j.jhydrol.2022.127970 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 74.00 Geographie Anthropogeographie: Allgemeines VZ 44.73 Geomedizin VZ AR 610 2022 0 |
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10.1016/j.jhydrol.2022.127970 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001936.pica (DE-627)ELV058198911 (ELSEVIER)S0022-1694(22)00545-5 DE-627 ger DE-627 rakwb eng 610 VZ 74.00 bkl 44.73 bkl Chen, Qiyu verfasserin aut Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. Deep learning Elsevier Hydrological structures Elsevier Generative adversarial networks Elsevier Heterogeneous patterns Elsevier Monte-Carlo simulation Elsevier Cui, Zhesi oth Liu, Gang oth Yang, Zixiao oth Ma, Xiaogang oth Enthalten in Elsevier Liu, Huiying ELSEVIER Neighborhood resources associated with frailty trajectories over time among community-dwelling older adults in China 2021 Amsterdam [u.a.] (DE-627)ELV007566492 volume:610 year:2022 pages:0 https://doi.org/10.1016/j.jhydrol.2022.127970 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 74.00 Geographie Anthropogeographie: Allgemeines VZ 44.73 Geomedizin VZ AR 610 2022 0 |
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10.1016/j.jhydrol.2022.127970 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001936.pica (DE-627)ELV058198911 (ELSEVIER)S0022-1694(22)00545-5 DE-627 ger DE-627 rakwb eng 610 VZ 74.00 bkl 44.73 bkl Chen, Qiyu verfasserin aut Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. Deep learning Elsevier Hydrological structures Elsevier Generative adversarial networks Elsevier Heterogeneous patterns Elsevier Monte-Carlo simulation Elsevier Cui, Zhesi oth Liu, Gang oth Yang, Zixiao oth Ma, Xiaogang oth Enthalten in Elsevier Liu, Huiying ELSEVIER Neighborhood resources associated with frailty trajectories over time among community-dwelling older adults in China 2021 Amsterdam [u.a.] (DE-627)ELV007566492 volume:610 year:2022 pages:0 https://doi.org/10.1016/j.jhydrol.2022.127970 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 74.00 Geographie Anthropogeographie: Allgemeines VZ 44.73 Geomedizin VZ AR 610 2022 0 |
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Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation |
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• A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. |
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• A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. |
abstract_unstemmed |
• A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. |
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