Nosratabadi, S.; Karoly, S.; Beszedes, B.; Felde, I.; Ardabili, S.; Mosavi, A. Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction. Preprints2020, 2020020353
APA Style
Nosratabadi, S., Karoly, S., Beszedes, B., Felde, I., Ardabili, S., & Mosavi, A. (2020). Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction. Preprints. https://doi.org/
Chicago/Turabian Style
Nosratabadi, S., Sina Ardabili and Amir Mosavi. 2020 "Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction" Preprints. https://doi.org/
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.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
