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
Gespeichert in:
| Autor*in: |
Chen, Qiyu [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| 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.] |
|---|---|
| Übergeordnetes Werk: |
volume:610 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jhydrol.2022.127970 |
|---|
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| 520 | |a • 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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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. |
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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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