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

He, Rui; Small, Mitchell J.; Scott, Ian J.; Olarinre, Motolani; Sandoval-Reyes, Mexitli; Ferrão, Paulo

http://hdl.handle.net/10362/158734

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
A_novel_domain_knowledge_informed_machine_learning_approach_for_modeling_solid_waste_management_systems.pdf		1.71 MB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Sandoval-Reyes, Mexitli

Ferrão, Paulo

Resumo(s)

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.

Descrição

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.

Palavras-chave

waste management decision support machine learning hybrid neural network physics-informed ML interpretable ML General Chemistry Environmental Chemistry SDG 9 - Industry, Innovation, and Infrastructure SDG 11 - Sustainable Cities and Communities SDG 12 - Responsible Consumption and Production