Synthetic generation of hydrologic time series based on nonparametric random generation
- Title
- Synthetic generation of hydrologic time series based on nonparametric random generation
- Author
- 김태웅
- Issue Date
- 2005-09
- Publisher
- ASCE-AMER SOC CIVIL ENGINEERS
- Citation
- JOURNAL OF HYDROLOGIC ENGINEERING, v. 10, No. 5, Page. 395-404
- Abstract
- Synthetic hydrologic time series can be used to quantify the uncertainty of a water resources system. Conventional parametric models, such as autoregressive moving average or Markovian models, assume that the variable under consideration is Gaussian. This assumption, however, is a shortcoming of parametric models and motivates the development of nonparametric approaches. Nonparametric models based on a kernel function have an innate low-order structure and are restricted to highly persistent variables. This study presented a seminonparametric (SNP) model that takes advantage of both parametric and nonparametric models to generate monthly precipitation and temperature in the Conchos River Basin in Mexico. By adopting a consistent and robust scheme from the Markovian model and a nonparametric mechanism to generate a distribution-free random, component, the SNP model reliably reproduced sample properties such as mean, variance, correlation, and multimodality in the probability density function.
- URI
- https://ascelibrary.org/doi/full/10.1061/%28ASCE%291084-0699%282005%2910%3A5%28395%29https://repository.hanyang.ac.kr/handle/20.500.11754/111524
- ISSN
- 1084-0699
- DOI
- 10.1061/(ASCE)1084-0699(2005)10:5(395)
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > CIVIL AND ENVIRONMENTAL ENGINEERING(건설환경공학과) > Articles
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