摘要
准确预报降水量对防洪防涝、水资源高效开发和利用起着至关重要的作用。由于降水量序列具有较强的非线性和突变性,使得传统的统计预测模型难以准确表征其时序特征。因此,本文提出了基于完全自适应噪声集合经验模态分解(CEEMDAN)和双向长短时记忆网络(Bi LSTM)的月降水量预测模型,通过对1960年1月~2013年12月的江西宜春气象站降水量数据进行预测,并与长短时记忆网络模型(LSTM)、BiLSTM、互补集合经验模态分解和长短时记忆网络模型(CEEMD-LSTM)、CEEMD-BiLSTM和CEEMDAN-LSTM模型进行了对比。结果表明:基于CEEMDAN法能够得到具有波动性更小的降水量分量序列,以此构建的Bi LSTM模型能够很好地捕捉降水量序列的变化特征;相较于其他模型,其预测结果的均方根误差、平均绝对误差和平均绝对百分比误差更小,且相关系数更大,即CEEMDAN-BiLSTM模型在降水量预测上具有更为良好的性能,该模型可为降水量预测提供一种新方法。
Accurate prediction of precipitation plays a significant role in flood control and efficient development and utilization of water resources.Due to the strong nonlinearity and variability of precipitation series,it is difficult for the traditional statistical prediction model to accurately characterize the temporal characteristics of precipitation series.Therefore,prediction model of monthly precipitation based on complementary ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and bi-directional long short-term memory(BiLSTM)was proposed in this paper.The precipitation monitoring data of Yichun meteorological station in Jiangxi province from January 1960 to December 2013 were used to establish the prediction model.The prediction results used by the CEEMDAN-BiLSTM model are compared with those of long short-term memory(LSTM),BiLSTM,complementary ensemble empirical mode decomposition(CEEMD)-LSTM,CEEMD-BiLSTM and CEEMDAN-LSTM models.The results show that the precipitation component series with less fluctuation can be obtained based on CEEMDAN method,and the BiLSTM model constructed by this method can capture the variation characteristics of precipitation series well.In addition,the root mean square error,mean absolute error and mean absolute percentage error of the prediction results used by CEEMDAN-BiLSTM are smaller,and the correlation coefficient is larger,indicating that CEEMDAN-BiLSTM model has better performance in precipitation prediction.The model proposed by this paper can provide a new idea for precipitation prediction.
作者
刘选
LIU Xuan(Jiangxi Yuanhuiqu Project Management Bureau,Xinyu Jiangxi,338025,China)
出处
《江西水利科技》
2023年第4期277-282,共6页
Jiangxi Hydraulic Science & Technology
