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

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.