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熊世为,张新民,段春锋,郁凌华,胡姗姗.滁州市汛期雨量多尺度时间变化特征及其GA-BP模型预测[J].沙漠与绿洲气象,2019,13(5):89~93
滁州市汛期雨量多尺度时间变化特征及其GA-BP模型预测
Multi-scale Time Variation Characteristics of Rainfall in Flood Season and Prediction of GA-BP Model in Chuzhou
投稿时间:2018-12-28  修订日期:2019-03-01
DOI:10.12057/j.issn.1002-0799.2019.05.013
中文关键词:  汛期雨量  多尺度时间变化  气候预测  GA-BP模型
英文关键词:Rainfall in flood season  Multi-scale time variation  Climate prediction  GA-BP model
基金项目:国家自然科学基金(41605068);中国气象局气候变化专项(CCSF201802)
作者单位E-mail
熊世为 滁州市气象局 18055033939@163.com 
张新民 滁州市气象局  
段春锋 安徽省气候中心  
郁凌华 滁州市气象局  
胡姗姗 滁州市气象局  
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中文摘要:
      本文利用集合经验模态分解EEMD方法分析了滁州地区汛期雨量的多尺度时间变化特征,以国家气候中心的百项环流指数作为预报因子并利用主成分分析进行预测因子优化,在此基础上建立基于GA-BP神经网络的汛期雨量气候预测模型,研究结果如下:1、该地区1961-2017年汛期雨量存在准2a、4a的年际尺度及准11a、16a的年代际尺度周期变化特征,总体趋势在1980年代之前、1980-2000年代及2000年代之后三个时段分别对应增多-减少-增多的趋势;2、基于GA-BP神经网络的汛期雨量气候预测效果良好,两种方案对滁州地区7个站的预测误差分别为122mm和144mm,其中利用主成分分析进行因子优化的方案预测效果更好。
英文摘要:
      This paper analyzed the multi-scale temporal variation characteristics of rainfall in Chuzhou during flood season by using the ensemble empirical mode decomposition EEMD method and established a GA-BP neural network climate forecasting model by taking circulation indices of the National Climate Center as forecasting factors and using principal component analysis to optimize the forecasting factors. The results showed that there are quasi-2a, quasi-4a inter-annual scale and quasi-11a, quasi-16a inter-decadal scale periodic variation characteristics of rainfall in the flood season of 1961-2017 in this area. The general trend corresponds to the trend of increase-decrease-increase in the three periods before 1980s, 1980-2000 and after 2000, respectively. Prediction of rainfall and climate in flood season based on GA-BP neural network has good effect. The prediction errors of the two schemes for seven stations in Chuzhou area are 122 mm and 144 mm, the scheme using principal component analysis to optimize the factors has better prediction effect.
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