可解释性
背景(考古学)
人工神经网络
持续性
计算机科学
集合(抽象数据类型)
机器学习
人工智能
领域(数学分析)
功能(生物学)
城市固体废物
风险分析(工程)
工程类
废物管理
生态学
生物
古生物学
数学分析
数学
进化生物学
程序设计语言
医学
作者
Rui He,Mitchell J. Small,Ian J. Scott,Motolani Olarinre,M. Sandoval-Reyes,Paulo Ferrão
标识
DOI:10.1021/acs.est.3c04214
摘要
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.
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