基于灰色关联度与BP神经网络模型的日参考作物腾发量预测

参考作物腾发量是制定灌溉用水计划、水量分配计划最基本、最重要的内容之一，其精确预测可以提高灌溉预报的精度。采用灰色系统理论中的关联分析方法，对影响作物腾发量的各个气象因素进行关联度分析，挑选出影响作物腾发量的主要气象因子，并以这些主要气象因子为输入向量，以参考作物腾发量为输出向量，建立作物腾发量与主要气象因子之间的BP神经网络预测模型。通过实例证明，该方法简单可行，预测精度比较高，能够满足实际生产需要。

Discuss on Daily Crop Evapotranspiration Forecast Based on Gray Relation Analysis and BP Neural Network

Reference crop evapotranspiration is the development of irrigation water plan, the most basic water allocation plan, and one of the most important indicators, the accurate prediction can improve the irrigation forecast precision. In this paper, the relational analysis in the gray system theory was applied to analyze the influences of various meteorological factors on evapotranspiration by selecting the impact of evapotranspiration in the main meteorological factors such as the input vector to refer to evapotranspiration as the output vector, to establish BP neural network model on evapotranspiration and the main meteorological factors. An example shows that the method is simple, relatively high prediction accuracy and can meet actual production needs.