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Krutikov, A. K., Meltsov, V. Y., Lapitsky, A. A., & Rostovtsev, V. S. (2020). EPGA-Implementation of a Prediction Module Based on a Generalized Regression Neural Network. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). https://doi.org/10.1109/EIConRus49466.2020.9039414

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• TITLE: Fitting of Water Requirement and Yield of Winter Wheat in North China Plain Based on Artificial Neural Network

  AUTHORS: Weibing Jia, Zhengying Wei, Lei Zhang, Yubin Zhang, Haoran Wei

  KEYWORDS: Winter Wheat, Water Requirement, Winter Wheat Yield, Artificial Neural Networks

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.9 No.4, April 12, 2021

  ABSTRACT: The fitting of water requirement and yield during the growth period of winter wheat can improve yield effectively and improve irrigation water use efficiency with a certain amount of resource input. This paper selects the irrigation amount, precipitation and yield of winter wheat at the Wuqiao Scientific Observation and Experimental Station. Fitting the water requirement and yield of winter wheat based on three types of artificial neural networks. This paper uses support vector machine (SVM), thought evolution algorithm to optimize BP neural network (MAE-BP) and generalized regression neural network (GRNN) to fit the water requirement and yield of two crops. The SVM is the model with the highest fitting accuracy among the three models, the RMSE, MAE, NS and R2 between predictive value and true value are 7.45 kg/hectares, 213.64 kg/hectares, 0.8086, 0.9409 respectively.