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Entropy 2014, 16(11), 5738-5752; doi:10.3390/e16115738

Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information

School of Civil Engineering, Shandong University, Jinan 250014, China
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Received: 13 July 2014 / Revised: 26 September 2014 / Accepted: 11 October 2014 / Published: 3 November 2014
(This article belongs to the Special Issue Entropy in Hydrology)
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Abstract

The intention of this paper is to evaluate the sensitivity of the Storm Water Management Model (SWMM) output to its input parameters. A global parameter sensitivity analysis is conducted in order to determine which parameters mostly affect the model simulation results. Two different methods of sensitivity analysis are applied in this study. The first one is the partial rank correlation coefficient (PRCC) which measures nonlinear but monotonic relationships between model inputs and outputs. The second one is based on the mutual information which provides a general measure of the strength of the non-monotonic association between two variables. Both methods are based on the Latin Hypercube Sampling (LHS) of the parameter space, and thus the same datasets can be used to obtain both measures of sensitivity. The utility of the PRCC and the mutual information analysis methods are illustrated by analyzing a complex SWMM model. The sensitivity analysis revealed that only a few key input variables are contributing significantly to the model outputs; PRCCs and mutual information are calculated and used to determine and rank the importance of these key parameters. This study shows that the partial rank correlation coefficient and mutual information analysis can be considered effective methods for assessing the sensitivity of the SWMM model to the uncertainty in its input parameters. View Full-Text
Keywords: sensitive analysis; SWMM model; mutual information; monte carlo simulation; Latin Hypercube Sampling ; partial rank correlation coefficient (PRCC); parameter ranking sensitive analysis; SWMM model; mutual information; monte carlo simulation; Latin Hypercube Sampling ; partial rank correlation coefficient (PRCC); parameter ranking
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, C.; Wang, W.; Xiong, J.; Chen, P. Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information. Entropy 2014, 16, 5738-5752.

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