Working Paper Article Version 1 This version is not peer-reviewed

Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction

Version 1 : Received: 23 February 2020 / Approved: 24 February 2020 / Online: 24 February 2020 (14:00:43 CET)

How to cite: Nosratabadi, S.; Karoly, S.; Beszedes, B.; Felde, I.; Ardabili, S.; Mosavi, A. Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction. Preprints 2020, 2020020353 Nosratabadi, S.; Karoly, S.; Beszedes, B.; Felde, I.; Ardabili, S.; Mosavi, A. Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction. Preprints 2020, 2020020353

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.

Subject Areas

Hybrid machine learning; artificial neural networks; imperialist competitive algorithm; gray wolf optimization; crop yield

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