【摘要】：針對(duì)太湖流域化肥用量和糧食產(chǎn)量數(shù)據(jù),利用BP神經(jīng)網(wǎng)絡(luò)算法,建立了糧食產(chǎn)量與化肥用量之間的關(guān)系模型,以指導(dǎo)化肥減施增效。共收集了1980—2014年共35 a太湖流域16個(gè)縣市每個(gè)縣市的單位面積化肥用量和單位面積糧食產(chǎn)量數(shù)據(jù)。通過(guò)自回歸滑動(dòng)平均模型(ARMA),對(duì)兩類數(shù)據(jù)進(jìn)行時(shí)間序列分析,對(duì)數(shù)據(jù)中存在的缺項(xiàng)進(jìn)行了填補(bǔ)。實(shí)驗(yàn)表明,對(duì)于單位面積糧食產(chǎn)量數(shù)據(jù),用ARMA(2,6)模型能夠達(dá)到較佳的填補(bǔ)效果,均方誤差小于0.2,R~20.85。對(duì)于單位面積化肥用量數(shù)據(jù),用ARMA(3,7)模型較優(yōu),均方誤差小于0.02,R~20.80。說(shuō)明ARMA模型數(shù)據(jù)填補(bǔ)效果較好。將填補(bǔ)后的不同縣的數(shù)據(jù)通過(guò)BP神經(jīng)網(wǎng)絡(luò)建立模型,描述了各縣市單位面積化肥用量和糧食產(chǎn)量的關(guān)聯(lián)關(guān)系。實(shí)驗(yàn)表明,該方法擬合的均方誤差小于0.12,R~20.80,說(shuō)明BP神經(jīng)網(wǎng)絡(luò)是一種準(zhǔn)確度較高的擬合方法。通過(guò)分析各縣擬合結(jié)果,表明化肥用量有閾值,化肥用量低于該閾值,糧食產(chǎn)量將會(huì)較快速增長(zhǎng),高于該閾值,糧食產(chǎn)量將不再增長(zhǎng),過(guò)多的施用化肥并不能取得高產(chǎn)。
[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，

本文編號(hào)：2522632