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.
Highlights
IntroductionThe effective arable land in this region accounts for up to 21% of the country’s total arable land, but it only accounts for 8% of the total water resources, including surface and groundwater
The North China Plain is important food production areas in China (Zhai, 2017)
Fitting the water requirement and yield of winter wheat based on three types of artificial neural networks
Summary
The effective arable land in this region accounts for up to 21% of the country’s total arable land, but it only accounts for 8% of the total water resources, including surface and groundwater. It is the most prominent place where the contradiction between the supply and demand of irrigation water in china, and the overexploitation of the groundwater level in the North China. The natural precipitation in the winter wheat growing season in this area can only meet about 30% of the normal water requirement demand, which cannot meet the healthy growth of winter wheat in North China (Bai, Wan, & Kang, 2018). The fitting of water requirement and yield during the growth period of winter wheat can increase yield and improve irrigation water use efficiency with a certain amount of resource input
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