中国农业气象

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基于卡尔曼滤波算法的稻纵卷叶螟短期预测模型

包云轩,陈心怡,谢晓金,王琳,陆明红   

  1. 1.南京信息工程大学气象灾害预报和评估协同创新中心,南京 210044;2.江苏省农业气象重点实验室/南京信息工程大学,南京 210044;3.农业部全国农业技术推广与服务中心,北京 100125
  • 收稿日期:2016-02-23 出版日期:2016-10-20 发布日期:2016-10-12
  • 作者简介:包云轩(1963-),博士,教授,主要研究方向为气候变化与防灾减灾、应用气象、病虫害测报学。E-mail:baoyx@nuist.edu.cn; baoyunxuan@163.com
  • 基金资助:
    国家自然科学基金面上项目(41475106;41075086);国家公益性行业(气象)科研专项(GYHY201306053);江苏省农业科技自主创新项目[SCX(12)3058];江苏省高校优势学科建设工程

Short-term Forecasting Models on Occurrence of Rice Leaf Roller Based on Kalman Filter Algorithm

BAO Yun-xuan, CHEN Xin-yi, XIE Xiao-jin, WANG Lin, LU Ming-hong   

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.Jiangsu Key Laboratory of Agricultural Meteorology/Nanjing University of Information Science & Technology, Nanjing 210044; 3.National Agricultural Technology Extension and Service Center, Ministry of Agricultural, Beijing 100125
  • Received:2016-02-23 Online:2016-10-20 Published:2016-10-12

摘要: 利用1994-2014年中国南方四大稻区(华南、西南、江岭和江淮稻区)代表性病虫测报站的稻纵卷叶螟逐候田间赶蛾量资料,筛选出影响各站稻纵卷叶螟发生量的关键气象因子,应用卡尔曼滤波方法分别对各站建立稻纵卷叶螟迁入期候发生量的卡尔曼短期预测模型,并计算模型的准确率、误差大小和稳定性。结果表明:(1)稻纵卷叶螟发生量与前一候和前两候的田间蛾量呈极显著正相关(P<0.01),与前一候的近地面最低气温、平均气温和最高气温呈极显著正相关(P<0.01),与前一候的地面气压呈极显著负相关(P<0.01)。(2)经1994-2011年的回检拟合和2012-2014年试报检验,卡尔曼模型的发生量预测综合平均误差为-88.63,平均绝对误差为217.72,均方根误差为605.04。发生量预测综合准确率为84.33%,平均历史拟合率为83.33%,各站卡尔曼模型的预报结果与实测值基本吻合,表明模型可以应用于稻纵卷叶螟候发生量的预测。

关键词: 稻纵卷叶螟, 气象因子, 卡尔曼滤波算法, 候发生量预报模型, 准确率

Abstract: In this paper, the pentad systematic investigation data of C. Medinalis at the four representative plant protection stations of four main rice-growing regions (including the rice-growing region of the south China, the rice-growing region of the southwestern China, the rice-growing region between the Nanling mountains and the Yantze River valley and the rice-growing region between the Yantze River valley and the Huaihe River valley) in China was collected from 1994 to 2014, the key meteorological factors influencing on C. Medinalis’ occurrence amount were screened out and Kalman filter algorithm was used to establish the short-term forecasting models of C. Medinalis’ pentad occurrence amount at the four plant protection stations, including Quanzhou in the Guangxi Zhuang Autonomous Region, Xiushan in Chongqing city, Xiangyin in Hunan province and Zhangjiagang in Jiangsu province in the immigration and damage period of C. Medinalis respectively. Based on the back substitution fittings and forecasting tests of the model, the errors and stability and accuracy rates of the Kalman model were calculated. The results showed as follows: (1) for four stations, the occurrence amount of C. medinalis in the present pentad was significantly and positively correlated (P<0.01) with the C. medinalis’s moth amounts of the preceding pentad and the preceding two pentads in the field respectively. There were significantly positive correlations (P<0.01) between the occurrence amounts of C. medinalis in the present pentad and the minimum air temperature, mean air temperature and maximum air temperature in the preceding pentad. But the pentad occurrence amount was significantly and negatively correlated with the surface pressure in the preceding pentad. (2) The back substitution fitting calculations from the Kalman model on the occurrence amount of C. Medinalis from 1994 to 2011 and the trial forecast tests from 2012 to 2014 showed that the comprehensive mean error (ME) of the occurrence amounts by the Kalman model was -88.63, the mean absolute error (MAE) was 217.72, the comprehensive root mean square error (RMSE) was 605.04, the comprehensive mean accuracies (MA) was 84.33%, and the fitting rate was 83.33%. The Kalman model’s forecasting results were basically consistent with measured values, which indicated that the model could be applied to the prediction of occurrence amount of C. medinalis .

Key words: Cnaphalocrocis medinalis Guenee, Meteorologic elements, Kalman Filter Algorithm, Pentad forecasting model of occurrence amount, Accuracy rate