Open Access

ARTICLE

Grain Yield Predict Based on GRA-AdaBoost-SVR Model

Diantao Hu, Cong Zhang^*, Wenqi Cao, Xintao Lv, Songwu Xie

Wuhan Polytechnic University, Wuhan, 430023, China

* Corresponding Author: Cong Zhang. Email:

Journal on Big Data 2021, 3(2), 65-76. https://doi.org/10.32604/jbd.2021.016317

Received 30 December 2020; Accepted 07 April 2021; Issue published 13 April 2021

Abstract

Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising application for grain yield datasets. However, the SVR algorithm’s own problems with the selection of parameters and kernel functions make the model less generalizable. Therefore, the Adaptive Boosting (AdaBoost) algorithm can be used. Using the SVR algorithm as a training method for base learners in the AdaBoost algorithm. Effectively address the generalization capability problem in SVR algorithms. In addition, to address the problem of sensitivity to anomalous samples in the AdaBoost algorithm, the GRA method is used to extract influence factors with higher correlation and reduce the number of anomalous samples. Finally, applying the GRA-AdaBoost-SVR model to grain yield forecasting in China. Experiments were conducted to verify the correctness of the model and to compare the effectiveness of several traditional models applied to the grain yield data. The results show that the GRA-AdaBoost-SVR algorithm improves the prediction accuracy, the model is smoother, and confirms that the model possesses better prediction performance and better generalization ability.

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