基于灰色关联分析与BP神经网络的农业灌溉预测  

Agricultural Irrigation Forecasting Based on Grey Relational Analysis and BP Neural Network

作　　者：刘晓艳[1,2] LIU Xiao-yan(Automated institute of Huaian college of Information Technology,Huaian 223003,China;The Engineering Technology Research and Development Center of Electronic Products Equipment Manufacturing of Jiangsu Province,Huai'an 223003,China)

机构地区：[1]淮安信息职业技术学院自动化学院,江苏淮安223003 [2]江苏电子产品装备制造工程技术研究开发中心,江苏淮安223003

出　　处：《数学的实践与认识》2020年第8期287-291,共5页Mathematics in Practice and Theory

基　　金：淮安市科技局自然科学研究计划(HABZ201807)“精准灌溉中神经网络预测与模糊决策模型研究”。

摘　　要：针对农业灌溉用水量预测存在的复杂性、不确定性和非线性等问题,提出一种基于灰色关联度分析与BP神经网络的灌溉需水量预测,首先,采用灰色关联度分析法,选取降水量、蒸发量、平均气温、日照时间、灌溉面积作为BP神经网络的输入因子;然后,根据各影响因子与灌溉用水量的对应关系,对模型训练;最后,将训练好的模型用于2007-2017年灌溉需水量预测中.结果表明,灰色关联-BP神经网络模型预测相对误差在1.81%~5.48%以内,可为农田灌溉预测提供科学依据.In view of the complexity,uncertainty and nonlinearity of agricultural irrigation water consumption forecasting,the irrigation forecasting based on grey relational analysis and BP neural network is proposed.Firstly,precipitation,evaporation,average temperature,sunshine time and atmospheric pressure are selected as the input variables of BP neural network based gray correlation analysis method,then,the model is trained based on the corresponding relationship between each impact factor and irrigation water use,finally,the trained model is used in the forecast of irrigation water use between 2007 and 2017.The results show that the relative error of prediction by the grey relation analysis-BP neural network model is between 1.81% and 5.48%,which can provide scientific guidance for farmland irrigation.

关 键 词：灰色关联度 BP神经网络 灌溉预测 

分 类 号：S274.4[农业科学—农业水土工程]