论文标题
混合机器学习模型的作物产量预测
Hybrid Machine Learning Models for Crop Yield Prediction
论文作者
论文摘要
作物产量的预测对于粮食安全决策，计划和贸易至关重要。当前研究的目的是基于混合机器学习方法提出新的作物产量预测模型。在这项研究中，评估了对作物产量预测的人工神经网络 - 帝国主义竞争算法（ANN-KIA）和人工神经网络灰狼优化器（ANN-GWO）模型的性能。根据结果，与ANN-KIA模型相比，ANN-GWO的R为0.48，RMSE为3.19，MEA为26.65，在作物产量预测中的性能更好。从业人员，研究人员或决策者可以使用该结果来用于粮食安全。
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.
