Preprint
Article
Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction
This version is not peer-reviewed.
Submitted:
23 February 2020
Posted:
24 February 2020
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Abstract
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 artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction is evaluated. According to the results, ANNGWO, 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.
Keywords:
Hybrid machine learning
; artificial neural networks
; imperialist competitive algorithm
; gray wolf optimization
; crop yield
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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