کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6410270 1629921 2015 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment
ترجمه فارسی عنوان
یک سیستم پیش بینی هیدرولوژیکی یک مجموعه ی چندجملهای برای ارجاع کارایی پارامترها و ارزیابی عدم قطعیت قوی
کلمات کلیدی
پیش بینی هیدرولوژیکی، استدلال حسی استنتاج پارامتر، گسترش هرج و مرج چندجملهای، متغیر تصادفی فازی، تحلیل عاملی واریانس،
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی


- A polynomial chaos ensemble hydrologic prediction system was developed.
- Possibilistic reasoning was infused into probabilistic parameter inference.
- Temporal and spatial variations in parameter sensitivities were revealed.
- Parametric interactions were explored depending on different hydrological metrics.
- Dimensionality reduction and polynomial chaos acceleration were achieved.

SummaryThis paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 530, November 2015, Pages 716-733
نویسندگان
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