Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production - Romeiko, Xiaobo Xue; Guo, Zhijian; Pang, Yulei; Lee, Eun Kyung; Zhang, Xuesong (Sustainability, 2020)
Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.
15 citations (Crossref) [2023-10-31] Number: 4
Keywords: Agriculture; Eutrophication; Global warming; Life cycle assessment; Machine learning; Midwest; Spatial assessment; United States; Zea mays; agricultural production; agriculture; crop production; dept.mat; eutrophication; global warming; life cycle analysis; life cycle assessment; machine learning; peer-reviewed; prediction; spatial analysis; spatial assessment

Learning from others: A low‐cost–high‐impact risk/opportunity identification technique - Williams, Brendan E.; Williams, Anne S.; Williams, Katherine M. (Journal of Healthcare Risk Management, 2020)
2 citations (Crossref) [2023-10-31]
Keywords: dept.mat; peer-reviewed

‘Is it wise to open up America again now?’ Learning from government responses to COVID-19 and economy reopen points in three Asia-Pacific countries - Yu, Jia; Pang, Yulei (British Journal of Healthcare Management, 2020)
With the continuously increasing number of new confirmed COVID-19 cases, many health experts worried about the possibility of a ‘second wave’ outbreak, which might cause more deaths and hit economies even worse. This article looks at the experiences of fighting COVID-19 from three Asia-Pacific countries and discusses whether it is a wise decision to open up America again at this time.
2 citations (Crossref) [2023-10-31] tex.ids: yuItWiseOpen2020a
Keywords: dept.eco; dept.mat; peer-reviewed

A regional Kronecker product and multiple-pair latin squares - Carrigan, Braxton; Hammer, James; Lorch, John (Discrete Mathematics, 2020)
We introduce a variant of the Kronecker product, called the regional Kronecker product, that can be used to build new, larger multiple-pair latin squares from existing multiple-pair latin squares. We present applications to the existence and orthogonality of multiple-pair latin squares. © 2019 Elsevier B.V.
2 citations (Crossref) [2023-10-31] tex.ids: carriganRegionalKroneckerProduct2020a tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85076148310 type: Article
Keywords: Factor pair latin squares; Multiple pair latin squares; Sudoku pair latin squares; dept.mat; peer-reviewed