Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO
Entropy applications in hydrometric network design problems have been extensively studied in the most recent decade. Although many studies have successfully found the optimal networks, there have been assumptions which could not be logically integrated into their methodology. One of the major assump...
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ftmdpi:oai:mdpi.com:/1099-4300/21/10/947/ 2023-08-20T04:05:57+02:00 Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO Jongho Keum Frezer Seid Awol Jacob Ursulak Paulin Coulibaly 2019-09-27 application/pdf https://doi.org/10.3390/e21100947 EN eng Multidisciplinary Digital Publishing Institute Multidisciplinary Applications https://dx.doi.org/10.3390/e21100947 https://creativecommons.org/licenses/by/4.0/ Entropy; Volume 21; Issue 10; Pages: 947 EnDEMO hydrometric network network design monitoring entropy ensemble uncertainty information theory multiobjective optimization Text 2019 ftmdpi https://doi.org/10.3390/e21100947 2023-07-31T22:38:56Z Entropy applications in hydrometric network design problems have been extensively studied in the most recent decade. Although many studies have successfully found the optimal networks, there have been assumptions which could not be logically integrated into their methodology. One of the major assumptions is the uncertainty that can arise from data processing, such as time series simulation for the potential stations, and the necessary data quantization in entropy calculations. This paper introduces a methodology called ensemble-based dual entropy and multiobjective optimization (EnDEMO), which considers uncertainty from the ensemble generation of the input data. The suggested methodology was applied to design hydrometric networks in the Nelson-Churchill River Basin in central Canada. First, the current network was evaluated by transinformation analysis. Then, the optimal networks were explored using the traditional deterministic network design method and the newly proposed ensemble-based method. Result comparison showed that the most frequently selected stations by EnDEMO were fewer and appeared more reliable for practical use. The maps of station selection frequency from both DEMO and EnDEMO allowed us to identify preferential locations for additional stations; however, EnDEMO provided a more robust outcome than the traditional approach. Text Churchill River MDPI Open Access Publishing Canada Entropy 21 10 947 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
EnDEMO hydrometric network network design monitoring entropy ensemble uncertainty information theory multiobjective optimization |
spellingShingle |
EnDEMO hydrometric network network design monitoring entropy ensemble uncertainty information theory multiobjective optimization Jongho Keum Frezer Seid Awol Jacob Ursulak Paulin Coulibaly Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO |
topic_facet |
EnDEMO hydrometric network network design monitoring entropy ensemble uncertainty information theory multiobjective optimization |
description |
Entropy applications in hydrometric network design problems have been extensively studied in the most recent decade. Although many studies have successfully found the optimal networks, there have been assumptions which could not be logically integrated into their methodology. One of the major assumptions is the uncertainty that can arise from data processing, such as time series simulation for the potential stations, and the necessary data quantization in entropy calculations. This paper introduces a methodology called ensemble-based dual entropy and multiobjective optimization (EnDEMO), which considers uncertainty from the ensemble generation of the input data. The suggested methodology was applied to design hydrometric networks in the Nelson-Churchill River Basin in central Canada. First, the current network was evaluated by transinformation analysis. Then, the optimal networks were explored using the traditional deterministic network design method and the newly proposed ensemble-based method. Result comparison showed that the most frequently selected stations by EnDEMO were fewer and appeared more reliable for practical use. The maps of station selection frequency from both DEMO and EnDEMO allowed us to identify preferential locations for additional stations; however, EnDEMO provided a more robust outcome than the traditional approach. |
format |
Text |
author |
Jongho Keum Frezer Seid Awol Jacob Ursulak Paulin Coulibaly |
author_facet |
Jongho Keum Frezer Seid Awol Jacob Ursulak Paulin Coulibaly |
author_sort |
Jongho Keum |
title |
Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO |
title_short |
Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO |
title_full |
Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO |
title_fullStr |
Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO |
title_full_unstemmed |
Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO |
title_sort |
introducing the ensemble-based dual entropy and multiobjective optimization for hydrometric network design problems: endemo |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/e21100947 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Churchill River |
genre_facet |
Churchill River |
op_source |
Entropy; Volume 21; Issue 10; Pages: 947 |
op_relation |
Multidisciplinary Applications https://dx.doi.org/10.3390/e21100947 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/e21100947 |
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Entropy |
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21 |
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10 |
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947 |
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1774716770095988736 |