基于遺傳優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的水稻氣象產(chǎn)量預(yù)報(bào)模型

doi:10.3969/2012jms.0103

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基于遺傳優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的水稻氣象產(chǎn)量預(yù)報(bào)模型
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                        10.3969/2012jms.0103
                    
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基金項(xiàng)目:國(guó)家自然科學(xué)基金面上項(xiàng)目(41175098);江蘇省氣象局開(kāi)放基金項(xiàng)目(KM201104)

A prediction model of rice meteorological yield based on neural networks optimized by genetic algorithm

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利用1951—2010年江蘇省水稻產(chǎn)量及同期14個(gè)氣象站點(diǎn)的逐日平均氣溫、降水資料,采用因子膨化及相關(guān)分析,研究了水稻氣象產(chǎn)量的影響因子及影響時(shí)段。在此基礎(chǔ)上建立了逐步回歸、PCA-BP神經(jīng)網(wǎng)絡(luò)以及PCA-GA-BP神經(jīng)網(wǎng)絡(luò)3種產(chǎn)量預(yù)報(bào)模型。結(jié)果表明:(1)7—9月份是水稻產(chǎn)量形成的關(guān)鍵時(shí)期,對(duì)氣溫、降水的變化最為敏感,氣溫對(duì)氣象產(chǎn)量的影響大于降水;(2)兩種神經(jīng)網(wǎng)絡(luò)模型預(yù)報(bào)效果好于回歸模型;(3)遺傳優(yōu)化的神經(jīng)網(wǎng)絡(luò)模型比未優(yōu)化模型的訓(xùn)練速度提高了70%左右,預(yù)報(bào)精度也提高了4.3%。

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With rice yield of Jiangsu Province during 1951 to 2010 and daily average temperature and precipitation data from 14 meteorological stations during the same period, this paper investigated the impact of hydrothermal resources on rice meteorological yield and the influence periods by using factors expanded method and related analysis, then established three kinds of yield forecast models on the basis of the meteorological yield: stepwise regression, PCA-BP neural networks and PCA-GA-BP neural network. The results showed that: (1)The critical period of yield formation of rice was July, August and September, rice yield was most sensitive to the changes of hydrothermal resources during this time, and the impact of temperature on meteorological yield was greater than precipitation; (2)two neural network models perform better than the regression model; (3)The training speed of network model which has been optimizated by genetic algorithm was about 70% faster than the model which has not been optimizated, while the forecast accuracy was also improved by 4.3%.

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羅夢(mèng)森,景元書(shū),熊世為.基于遺傳優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的水稻氣象產(chǎn)量預(yù)報(bào)模型.氣象科學(xué),2012,32(6):665-670 LUO Mengsen, JING Yuanshu, XIONG Shiwei. A prediction model of rice meteorological yield based on neural networks optimized by genetic algorithm. Journal of the Meteorological Sciences,2012,32(6):665-670

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收稿日期:2011-11-09
最后修改日期:2012-04-23
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在線發(fā)布日期: 2013-01-05
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抽查舔水白紧大视频,冲喜娘子超甜哒激情五月,亚洲黄色图片偷拍网站,亚洲三级黄片免费播放,91在线观看喷潮红桃,日韩精品在线观看一区二区,久久久久久久久久久久黄色片子,日产精品99久久久久久久久,欧洲亚洲另类图片小说