基于支持向量机的辽宁省粮食产量预测
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引用本文:张文政,孙德山,王 玥,张 蕾.基于支持向量机的辽宁省粮食产量预测[J].经济数学,2019,(1):96-99
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作者单位
张文政,孙德山,王 玥,张 蕾 (辽宁师范大学 数学学院辽宁 大连 116029) 
中文摘要:随着中国经济的不断发展,城市化进程不断推进,总人口逐年增加;农村人口逐年减少,粮食的需求量逐年增加,某些贫困地区已经出现粮食短缺的状况.本文选取了1986年-2016年辽宁省年粮食总产量、有效灌溉面积、农业化肥施用量、农业机械总动力、播种面积以及受灾面积等相关数据.利用支持向量机回归、线性回归,随机森林三种方法,对辽宁省粮食产量进行了预测,并比较了三种方法预测的精准度.
中文关键词:支持向量机  线性回归  随机森林  核函数
 
Prediction of Grain Production in Liaoning Province Based on Support Vector Machine
Abstract:With the continuous development of China's economy, the process of urbanization is advancing, and the total population is increasing year by year, but the population of rural areas is decreasing year after year. As time goes on ,the demand for grain has increased , and some poor areas have already appeared in the condition of grain shortage. In this paper, the total annual grain output, the effective irrigation area, the amount of agricultural fertilizer application, the total power of agricultural machinery, the sown area and the affected area of Liaoning province from 1986 to 2016 were selected. By using three methods of support vector machine regression, linear regression and random forest, the grain yield of Liaoning province was predicted, and the accuracy of the three methods was compared.
keywords:support vector machine  linear regression  random forest  kernel function
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