Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information
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 sensi...
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MDPI AG
2014-11-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/16/11/5738 |
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| author | Chuanqi Li Wei Wang Jianzhi Xiong Pengyu Chen |
| author_facet | Chuanqi Li Wei Wang Jianzhi Xiong Pengyu Chen |
| author_sort | Chuanqi Li |
| collection | DOAJ |
| description | 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. |
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| language | English |
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| publishDate | 2014-11-01 |
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| spelling | doaj.art-fab8cb871ade471596198b36922bc7ab2022-12-22T03:10:33ZengMDPI AGEntropy1099-43002014-11-0116115738575210.3390/e16115738e16115738Sensitivity Analysis for Urban Drainage Modeling Using Mutual InformationChuanqi Li0Wei Wang1Jianzhi Xiong2Pengyu Chen3School of Civil Engineering, Shandong University, Jinan 250014, ChinaSchool of Civil Engineering, Shandong University, Jinan 250014, ChinaSchool of Civil Engineering, Shandong University, Jinan 250014, ChinaSchool of Civil Engineering, Shandong University, Jinan 250014, ChinaThe 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.http://www.mdpi.com/1099-4300/16/11/5738sensitive analysisSWMM modelmutual informationmonte carlo simulationLatin Hypercube Samplingpartial rank correlation coefficient (PRCC)parameter ranking |
| spellingShingle | Chuanqi Li Wei Wang Jianzhi Xiong Pengyu Chen Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information Entropy sensitive analysis SWMM model mutual information monte carlo simulation Latin Hypercube Sampling partial rank correlation coefficient (PRCC) parameter ranking |
| title | Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information |
| title_full | Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information |
| title_fullStr | Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information |
| title_full_unstemmed | Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information |
| title_short | Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information |
| title_sort | sensitivity analysis for urban drainage modeling using mutual information |
| topic | sensitive analysis SWMM model mutual information monte carlo simulation Latin Hypercube Sampling partial rank correlation coefficient (PRCC) parameter ranking |
| url | http://www.mdpi.com/1099-4300/16/11/5738 |
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