基于ε-SVR的粮食产量预测模型及应用  

Modeling and prediction of foodstuff output based on ε-SVR method.

作　　者：董美双[1] 何欢[2] 童晓星[3] 

机构地区：[1]浙江大学城市学院,浙江杭州310015 [2]浙江工商大学经济学院,浙江杭州310018 [3]浙江大学生物系统工程与食品科学学院,浙江杭州310029

出　　处：《浙江大学学报（农业与生命科学版）》2009年第4期439-443,共5页Journal of Zhejiang University:Agriculture and Life Sciences

基　　金：国家自然科学基金资助项目(30671213);教育部青年教师教学科研基金资助项目

摘　　要：提出一种基于支持向量回归的粮食产量预测方法,以浙江省近14年的粮食产量统计数据作为分析对象,选择影响粮食产量的农业从业人员、谷物播种面积、粮食总种植面积、农村机械总动力、农村用电量、受灾面积、成灾面积、上一年粮食收购价格、有效灌溉面积和化肥施用量等10个因素,用1991—2002年的产量数据进行建模,得到98.47%的拟合精度.应用这一模型,对2003和2004年度的粮食产量进行预测,分别达到97.5%和95.8%的预测精度.说明该方法适合用于粮食产量分析和短期预测,为粮食产量预测提供了一种新方法.A new foodstuff output prediction method was investigated based on e-support vector regression （ε-SVR）. The statistical data of foodstuff output of Zhejiang Province in the last 14 years were applied as the analytical matrix. The influence factors of foodstuff output were the following 10 aspects, including agricultural practitioners, cereal sown area, total foodstuff plant area, total power of farm machinery, electricity consumed in rural area, area of affected crops, disaster-affected areas, foodstuff purchasing price of preceding year, effective irrigated area and fertilizing amount. The foodstuff output data of year 1991--2002 were applied as calibration set to develop ε-SVR model, and the prediction precision was 98.47 %. The foodstuff output of year 2003 and 2004 was used as validation set, and the prediction precision by the above developed ε-SVR model were 97.5% and 95.8% for year 2003 and 2004, respectively. The results above indicate that ε-SVR is suitable for the analysis and short-term prediction of foodstuff output, and ε-SVR supplies a new method for the prediction of foodstuff output.

关 键 词：ε-SVR 粮食产量 短期预测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] S114[自动化与计算机技术—控制科学与工程]