کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6331001 | 1619787 | 2014 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling
ترجمه فارسی عنوان
یک رویکرد رگرسیون بیزی برای ارزیابی عدم قطعیت در مدل سازی خنک کننده آلاینده
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کلمات کلیدی
عدم قطعیت مدل، کیفیت طوفان شستشو آلاینده، تجزیه و تحلیل بیزی، شبیه سازی مونت کارلو، فرایندهای آلاینده طوفان آب،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
شیمی زیست محیطی
چکیده انگلیسی
Due to knowledge gaps in relation to urban stormwater quality processes, an in-depth understanding of model uncertainty can enhance decision making. Uncertainty in stormwater quality models can originate from a range of sources such as the complexity of urban rainfall-runoff-stormwater pollutant processes and the paucity of observed data. Unfortunately, studies relating to epistemic uncertainty, which arises from the simplification of reality are limited and often deemed mostly unquantifiable. This paper presents a statistical modelling framework for ascertaining epistemic uncertainty associated with pollutant wash-off under a regression modelling paradigm using Ordinary Least Squares Regression (OLSR) and Weighted Least Squares Regression (WLSR) methods with a Bayesian/Gibbs sampling statistical approach. The study results confirmed that WLSR assuming probability distributed data provides more realistic uncertainty estimates of the observed and predicted wash-off values compared to OLSR modelling. It was also noted that the Bayesian/Gibbs sampling approach is superior compared to the most commonly adopted classical statistical and deterministic approaches commonly used in water quality modelling. The study outcomes confirmed that the predication error associated with wash-off replication is relatively higher due to limited data availability. The uncertainty analysis also highlighted the variability of the wash-off modelling coefficient k as a function of complex physical processes, which is primarily influenced by surface characteristics and rainfall intensity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Science of The Total Environment - Volumes 479â480, 1 May 2014, Pages 233-240
Journal: Science of The Total Environment - Volumes 479â480, 1 May 2014, Pages 233-240
نویسندگان
Prasanna Egodawatta, Khaled Haddad, Ataur Rahman, Ashantha Goonetilleke,