基于BP神经网络的粮食产量与化肥用量相关性研究
发布时间：2019-08-03 15:03
【摘要】：针对太湖流域化肥用量和粮食产量数据,利用BP神经网络算法,建立了粮食产量与化肥用量之间的关系模型,以指导化肥减施增效。共收集了1980—2014年共35 a太湖流域16个县市每个县市的单位面积化肥用量和单位面积粮食产量数据。通过自回归滑动平均模型(ARMA),对两类数据进行时间序列分析,对数据中存在的缺项进行了填补。实验表明,对于单位面积粮食产量数据,用ARMA(2,6)模型能够达到较佳的填补效果,均方误差小于0.2,R~20.85。对于单位面积化肥用量数据,用ARMA(3,7)模型较优,均方误差小于0.02,R~20.80。说明ARMA模型数据填补效果较好。将填补后的不同县的数据通过BP神经网络建立模型,描述了各县市单位面积化肥用量和粮食产量的关联关系。实验表明,该方法拟合的均方误差小于0.12,R~20.80,说明BP神经网络是一种准确度较高的拟合方法。通过分析各县拟合结果,表明化肥用量有阈值,化肥用量低于该阈值,粮食产量将会较快速增长,高于该阈值,粮食产量将不再增长,过多的施用化肥并不能取得高产。
[Abstract]:Based on the data of chemical fertilizer dosage and grain yield in Taihu Lake Basin, the relationship model between grain yield and chemical fertilizer dosage was established by using BP neural network algorithm to guide the reduction and efficiency of chemical fertilizer application. From 1980 to 2014, the data of chemical fertilizer per unit area and grain yield per unit area in 16 counties and cities of Taihu Lake Basin from 1980 to 2014 were collected. The time series analysis of the two kinds of data is carried out by using the autoregression moving average model (ARMA), and the missing items in the data are filled. The experimental results show that ARMA (2, 6) model can achieve better filling effect for grain yield data per unit area, the mean square error is less than 0.2, R 鈮，
本文编号：2522632
[Abstract]:Based on the data of chemical fertilizer dosage and grain yield in Taihu Lake Basin, the relationship model between grain yield and chemical fertilizer dosage was established by using BP neural network algorithm to guide the reduction and efficiency of chemical fertilizer application. From 1980 to 2014, the data of chemical fertilizer per unit area and grain yield per unit area in 16 counties and cities of Taihu Lake Basin from 1980 to 2014 were collected. The time series analysis of the two kinds of data is carried out by using the autoregression moving average model (ARMA), and the missing items in the data are filled. The experimental results show that ARMA (2, 6) model can achieve better filling effect for grain yield data per unit area, the mean square error is less than 0.2, R 鈮，
本文编号：2522632
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