Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production

Xiaobo Xue Romeiko, Zhijian Guo, Yulei Pang, Eun Kyung Lee and Xuesong Zhang
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Xiaobo Xue Romeiko: Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, George Education Center, Rensselaer, NY 12144, USA
Zhijian Guo: Department of Mathematics, University at Albany, State University of New York, Albany, NY 12222, USA
Yulei Pang: Department of Mathematics, Southern Connecticut State University, 501 Crescent Street, New Haven, CT 06515, USA
Eun Kyung Lee: Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, George Education Center, Rensselaer, NY 12144, USA
Xuesong Zhang: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA

Sustainability, 2020, vol. 12, issue 4, 1-19

Abstract: 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.

Keywords: life cycle assessment; global warming; eutrophication; machine learning; spatial assessment; agriculture (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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