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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ftdoajarticles:oai:doaj.org/article:446a796599bc4f6098bae30fdd452d73 2023-05-15T15:55:11+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 2019-09-01T00:00:00Z https://doi.org/10.3390/e21100947 https://doaj.org/article/446a796599bc4f6098bae30fdd452d73 EN eng MDPI AG https://www.mdpi.com/1099-4300/21/10/947 https://doaj.org/toc/1099-4300 1099-4300 doi:10.3390/e21100947 https://doaj.org/article/446a796599bc4f6098bae30fdd452d73 Entropy, Vol 21, Iss 10, p 947 (2019) endemo hydrometric network network design monitoring entropy ensemble uncertainty information theory multiobjective optimization Science Q Astrophysics QB460-466 Physics QC1-999 article 2019 ftdoajarticles https://doi.org/10.3390/e21100947 2022-12-30T20:41:36Z 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. Article in Journal/Newspaper Churchill River Directory of Open Access Journals: DOAJ Articles Canada Entropy 21 10 947 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
endemo hydrometric network network design monitoring entropy ensemble uncertainty information theory multiobjective optimization Science Q Astrophysics QB460-466 Physics QC1-999 |
spellingShingle |
endemo hydrometric network network design monitoring entropy ensemble uncertainty information theory multiobjective optimization Science Q Astrophysics QB460-466 Physics QC1-999 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 Science Q Astrophysics QB460-466 Physics QC1-999 |
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 |
Article in Journal/Newspaper |
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 |
MDPI AG |
publishDate |
2019 |
url |
https://doi.org/10.3390/e21100947 https://doaj.org/article/446a796599bc4f6098bae30fdd452d73 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Churchill River |
genre_facet |
Churchill River |
op_source |
Entropy, Vol 21, Iss 10, p 947 (2019) |
op_relation |
https://www.mdpi.com/1099-4300/21/10/947 https://doaj.org/toc/1099-4300 1099-4300 doi:10.3390/e21100947 https://doaj.org/article/446a796599bc4f6098bae30fdd452d73 |
op_doi |
https://doi.org/10.3390/e21100947 |
container_title |
Entropy |
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21 |
container_issue |
10 |
container_start_page |
947 |
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1766390517692628992 |