کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4377274 | 1303418 | 2010 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An Integrated Monte Carlo Methodology for the calibration of water quality models
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
بوم شناسی، تکامل، رفتار و سامانه شناسی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
This paper proposes an Integrated Monte Carlo Methodology (IMCM) to solve the parameter estimation problem in water quality models. The methodology is based on Bayesian approach and Markov Chain Monte Carlo techniques and it operates by means of four modules: Markov Chain Monte Carlo (MCMC), Moving Feasible Ranges (MFR), Statistical Analysis of the Joint Posterior Distribution (SAD) and Uncertainty Propagation Analysis (UPA). The main innovation of the new proposal lies in the combination of MCMC and MFR modules which provides the joint posterior distribution of the calibrated parameters following the classical Bayesian approach. While MCMC module, based on Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm, is specially designed to sample complex joint posterior shapes within certain parameter ranges, the MFR readjusts these ranges until the coverage of the feasible parameter space is guaranteed. Once the joint posterior distribution is properly defined, the SAD provides the parameter statistics and the UPA performs an analysis of the uncertainty propagation through the model. The possibilities of the new proposal have been tested on the basis of a simple model featuring different activated sludge batch experiments. IMCM has been implemented in Matlab and it is prepared to be easily connected to any software package.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecological Modelling - Volume 221, Issue 22, 10 November 2010, Pages 2656-2667
Journal: Ecological Modelling - Volume 221, Issue 22, 10 November 2010, Pages 2656-2667
نویسندگان
C. Martin, E. Ayesa,