首页|Studies from State University of New York (SUNY) Reveal New Findings on Machine Learning (A Review of Machine Learning Applications In Life Cycle Assessment Studies)
Studies from State University of New York (SUNY) Reveal New Findings on Machine Learning (A Review of Machine Learning Applications In Life Cycle Assessment Studies)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
Researchers detail new data in Machine Learning. According to news reporting originating from Albany, New York, by NewsRx correspondents, research stated, “Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA.” Financial supporters for this research include USDA Agricultural Research Service, National Science Foundation (NSF), NIH National Institute on Aging (NIA), National Aeronautics & Space Administration (NASA). Our news editors obtained a quote from the research from the State University of New York (SUNY), “Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment.”
AlbanyNew YorkUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningState University of New York (SUNY)
NETL
NSTL
Elsevier
