arXiv 2005.041552020-05cs.NEcs.LGstat.ML

作物产量预测的混合机器学习模型

Hybrid Machine Learning Models for Crop Yield Prediction

School of the built Environment（建筑环境学院）；Alba Regia Technical Faculty（阿尔巴雷吉亚技术学院）；Oxford Brookes University（牛津布鲁克斯大学）；Obuda University（欧布达大学）；University of Pannonia（潘诺尼亚大学）；J. Selye University（塞耶大学）；Bauhaus Universität Weimar（魏玛包豪斯大学）

Saeed Nosratabadi, Felde Imre, Karoly Szell, Sina Ardabili, Bertalan Beszedes, Amir Mosavi

备注：: 5 pages, 2 figures, 3 tables

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摘要：

作物产量预测对于粮食安全决策、规划和贸易至关重要。本研究的目的是提出新的作物产量预测模型的基础上混合机器学习方法。在这项研究中，人工神经网络-帝国主义竞争算法（ANN-ICA）和人工神经网络-灰狼优化（ANN-GWO）模型的作物产量预测的性能进行了评估。结果表明，与ANN-ICA模型相比，ANN-GWO模型的相关系数为0.48，均方根误差为3.19，平均误差为26.65，在作物产量预测中表现出更好的性能。研究结果可供从业人员、研究人员或政策制定者用于粮食安全。

英文摘要：

Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of the artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction are evaluated. According to the results, ANN-GWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.

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