HUANG Haixun,ZHOU Yunjun,ZENG Yong,et al.Meteorological Forecast of Sugarcane Production in Guigang, Guangxi[J].Journal of Chengdu University of Information Technology,2020,35(05):554-559.[doi:10.16836/j.cnki.jcuit.2020.05.013]
广西贵港甘蔗产量气象预报
- Title:
- Meteorological Forecast of Sugarcane Production in Guigang, Guangxi
- 文章编号:
- 2096-1618(2020)05-0554-06
- Keywords:
- atmospheric sciences; agricultural meteorology; stepwise regression; BP neural network; grey prediction
- 分类号:
- S162.4
- 文献标志码:
- A
- 摘要:
- 为提高威宁地区甘蔗产量,利用1995-2013年贵港市历史气象资料,通过SPSS对相对气象产量和各气象因子进行Pearson相关性分析,得出:4月的温度以及日照时数、12月的日照时数、1月的降水与甘蔗相对气象产量的相关性分别为-0.352、-0.407、-0.399、0.445,分别经过0.2、0.1、0.1、0.1水平的显著性检验,是影响甘蔗产量的主要因子; 通过逐步回归分析、BP神经网络方法建立甘蔗产量预报模型并作对比分析,得出:神经网络模式在历史产量拟合效果(拟合平均误差0.0031%)以及预测效果(预测相对误差5.3758%)均好于其他两种方法。
- Abstract:
- In order to increase sugarcane production in Weining area, using the historical meteorological data of Guigang City from 1995 to 2013, the pearson correlation of the relative meteorological yield and various meteorological factors were processed through SPSS. The results are as follows: temperature and hours of sunshine in April, the The hours of sunshine in December and precipitation in January were the main factors affecting sugarcane yield. Their correlations of with the relative meteorological yields of sugarcane are -0.352, -0.407, -0.399,0.445, which have passed the significance test of 0.2, 0.1, 0.1 and 0.1, respectively.The stepwise regression analysis and BP neural network method were used to establish a sugarcane yield forecasting model and made a comparative analysis. It is concluded that the neural network model is better than the other two methods in the historical yield fitting effect(the average error of the fitting is 0.0031%)and the prediction effect(the relative error of the prediction is 5.3758%).
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备注/Memo
收稿日期:2019-11-19
基金项目:国家自然科学基金资助项目(41875169); 国家重点研发计划资助项目(2018YFC1505702); 贵州省科技计划资助项目(黔科合支撑[2019]2387号); 四川省教育厅资助项目(16CZ0021)