A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems

Detalhes bibliográficos
Autor(a) principal: He, Rui
Data de Publicação: 2023
Outros Autores: Small, Mitchell J., Scott, Ian J., Olarinre, Motolani, Sandoval-Reyes, Mexitli, Ferrão, Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/158734
Resumo: He, R., Small, M. J., Scott, I. J., Olanrinre, M., Sandoval-Reyes, M., & Ferrão, P. (2023). A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems. Environmental Science & Technology, A-J. https://doi.org/10.1021/acs.est.3c04214---This research is supported by the Mao Yisheng Fellowship of Carnegie Mellon University to Rui He and through the CMU-Portugal project “Bee2Waste Crypto” (IDT-COP 45933). I.S. acknowledges the financial support provided by Fundação para a Ciência e a Tecnologia (FCT) Portugal under Project UIDB/0415s2/2020 - Centro de Investigação em Gestão de Informação (MagIC). The authors thank Dr. Scott Matthews for reviewing and editing this paper.
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spelling A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systemswaste managementdecision supportmachine learninghybrid neural networkphysics-informed MLinterpretable MLChemistry(all)Environmental ChemistrySDG 9 - Industry, Innovation, and InfrastructureSDG 11 - Sustainable Cities and CommunitiesSDG 12 - Responsible Consumption and ProductionHe, R., Small, M. J., Scott, I. J., Olanrinre, M., Sandoval-Reyes, M., & Ferrão, P. (2023). A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems. Environmental Science & Technology, A-J. https://doi.org/10.1021/acs.est.3c04214---This research is supported by the Mao Yisheng Fellowship of Carnegie Mellon University to Rui He and through the CMU-Portugal project “Bee2Waste Crypto” (IDT-COP 45933). I.S. acknowledges the financial support provided by Fundação para a Ciência e a Tecnologia (FCT) Portugal under Project UIDB/0415s2/2020 - Centro de Investigação em Gestão de Informação (MagIC). The authors thank Dr. Scott Matthews for reviewing and editing this paper.Sustainability challenges, such as solid waste management, are usually scientifically complex and data scarce, which makes them not amenable to science-based analytical forms or data-intensive learning paradigms. Deep integration between data science and sustainability science in highly complementary manners offers new opportunities for tackling these conundrums. This study develops a novel hybrid neural network (HNN) model that imposes the holistic decision-making context of solid waste management systems (SWMS) on a traditional neural network (NN) architecture. Equipped with adaptable hybridization designs of hand-crafted model structure, constrained or predetermined parameters, and a customized loss function, the HNN model is capable of learning various technical, economic, and social aspects of SWMS from a small and heterogeneous data set. In comparison, the versatile HNN model not only outperforms traditional NN models in convergence rates, which leads to a 22% lower mean testing error of 0.20, but also offers superior interpretability. The HNN model is capable of generating insights into the enabling factors, policy interventions, and driving forces of SWMS, laying a solid foundation for data-driven decision making.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNHe, RuiSmall, Mitchell J.Scott, Ian J.Olarinre, MotolaniSandoval-Reyes, MexitliFerrão, Paulo2023-11-212024-09-30T00:00:00Z2023-11-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/158734eng0013-936XPURE: 73075499https://doi.org/10.1021/acs.est.3c04214info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:41:16Zoai:run.unl.pt:10362/158734Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:17.240489Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
title A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
spellingShingle A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
He, Rui
waste management
decision support
machine learning
hybrid neural network
physics-informed ML
interpretable ML
Chemistry(all)
Environmental Chemistry
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
SDG 12 - Responsible Consumption and Production
title_short A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
title_full A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
title_fullStr A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
title_full_unstemmed A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
title_sort A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems
author He, Rui
author_facet He, Rui
Small, Mitchell J.
Scott, Ian J.
Olarinre, Motolani
Sandoval-Reyes, Mexitli
Ferrão, Paulo
author_role author
author2 Small, Mitchell J.
Scott, Ian J.
Olarinre, Motolani
Sandoval-Reyes, Mexitli
Ferrão, Paulo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv He, Rui
Small, Mitchell J.
Scott, Ian J.
Olarinre, Motolani
Sandoval-Reyes, Mexitli
Ferrão, Paulo
dc.subject.por.fl_str_mv waste management
decision support
machine learning
hybrid neural network
physics-informed ML
interpretable ML
Chemistry(all)
Environmental Chemistry
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
SDG 12 - Responsible Consumption and Production
topic waste management
decision support
machine learning
hybrid neural network
physics-informed ML
interpretable ML
Chemistry(all)
Environmental Chemistry
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
SDG 12 - Responsible Consumption and Production
description He, R., Small, M. J., Scott, I. J., Olanrinre, M., Sandoval-Reyes, M., & Ferrão, P. (2023). A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems. Environmental Science & Technology, A-J. https://doi.org/10.1021/acs.est.3c04214---This research is supported by the Mao Yisheng Fellowship of Carnegie Mellon University to Rui He and through the CMU-Portugal project “Bee2Waste Crypto” (IDT-COP 45933). I.S. acknowledges the financial support provided by Fundação para a Ciência e a Tecnologia (FCT) Portugal under Project UIDB/0415s2/2020 - Centro de Investigação em Gestão de Informação (MagIC). The authors thank Dr. Scott Matthews for reviewing and editing this paper.
publishDate 2023
dc.date.none.fl_str_mv 2023-11-21
2023-11-21T00:00:00Z
2024-09-30T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/158734
url http://hdl.handle.net/10362/158734
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0013-936X
PURE: 73075499
https://doi.org/10.1021/acs.est.3c04214
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv 10
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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