摘要
为了避免农作物遇霜后遭受冻害,本研究采用草面温度对霜进行预测。利用连云港气象观测站2014-2016年逐时气象要素,包括气温、0 cm地温、露点温度、水汽压、气压以及2 min平均风速等气象要素作为影响连云港地区草面温度的关键因子,并以这6个要素作为属性特征,以草温作为标志量构建训练样本集,结合KNN数据挖掘算法构建草温预测模型,并根据草温判别是否有霜出现。结果表明:基于该算法构建的草温预测模型效果较好,预报平均误差1.2℃;根据草温预测霜的准确率高达90.2%,尤其对初终霜的预报具有很好的指示意义。因此,引入草温作为霜的预报指标,对于避免农作物遭受霜害具有十分重要的意义。
To avoid freezing damage to crops after frost, we used grassland temperature to predict frost. Based on hourly meteorological data from Lianyungang Meteorological Observatory during 2014-2016, including temperature, 0 cm ground temperature, dew point temperature, water vapor pressure, air pressure and 2 min average wind speed, which were the key factors affecting the grassland temperature in Lianyungang, we took these 6 elements as attribute features, and constructed a training sample set with grass temperature as a marker. The KNN data mining algorithm is combined to construct a grass temperature prediction model, and the frost occurrence is judged according to the grass temperature. The results showed that: the grass temperature prediction model based on the algorithm could achieve better prediction effect, the forecast average error was 1.2℃;according to the grass temperature, the accuracy of frost forecast reached 90.2%,which had a good indication especially for the prediction of the initial and the last frost. Therefore, the introduction of grass temperature as a prediction index of frost is of significance for avoiding crop damage.
作者
郝玲
史逸民
史达伟
顾春雷
Hao Ling;Shi Yimin;Shi Dawei;Gu Chunlei(Lianyungang Meteorological Bureau,Lianyungang Jiangsu 222006)
出处
《中国农学通报》
2020年第15期94-99,共6页
Chinese Agricultural Science Bulletin
基金
江苏省气象局青年科研基金“连云港地区基于草温的霜的预报模型”(Q201708)
江苏省预报员专项“地面辐合线在江苏省强对流预报预警中的应用”(JSYBY201810)。
关键词
农作物
霜
草面温度
关键因子
KNN算法
预测模型
crop
frost
grassland temperature
key factor
K-Nearest Neighbor(KNN)
prediction model
