基于混沌理论的降水量预测方法研究  

Predicting Monthly Precipitation Using Chaotic Model

作　　者：舒涛 路昊天 曹景轩 叶唐进 陶伟 付润艺 李豪[5,6,7] SHU Tao;LU Haotian;CAO Jingxuan;YE Tangjin;TAO Wei;FU Runyi;LI Hao(College of Marine Geosciences,Ocean University of China,Qingdao 266100,China;College of Earth Sciences,Guilin University of Technology,G uilin 541004,China;College of Engineering,Tibet University,Lhasa 850000,China;Department of Construction Engineering,Dalian University of Technology,Dalian 116024,China;Key Laboratory of Mountain Hazards and Earth Surface Process,Chinese Academy of Sciences,Chengdu 610041,China;Institute of Mountain Hazards and Environment,Chinese Academy of Sciences&Ministry of Water Conservancy,Chengdu 610041,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国海洋大学海洋地球科学学院,山东青岛266100 [2]桂林理工大学地球科学学院,广西桂林541004 [3]西藏大学工学院,拉萨850000 [4]大连理工大学建设工程学部,辽宁大连116024 [5]中国科学院山地灾害与地表过程重点实验室,成都610041 [6]中国科学院水利部成都山地灾害与环境研究所,成都610041 [7]中国科学院大学,北京100049

出　　处：《灌溉排水学报》2022年第3期83-91,共9页Journal of Irrigation and Drainage

基　　金：广西研究生教育创新计划项目(YCSW2021203);大学生创新实验项目(202010694008);大学生创新实验项目(2020XCX011)。

摘　　要：【目的】得到精确度较高的月降水量预测值。【方法】首先利用C-C关联积分法来确定波密站月降水量非线性系统的时间延迟τ和嵌入维数m,再对月降水量时间序列进行相空间重构,并利用小数据量法求取Lyapunov指数来判断月降水量时间序列的混沌特征,然后构建Volterra模型分别进行短期5a和长期15a降水量预测,将其预测小波预测模型和SVR预测模型的预测值对比,最后对Volterra短期预测模型进行叠加预测误差分析和模型推广分析。【结果】Volterra模型对混沌特征明显的月降水量进行短期预测时,其MAPE和EC分别为4.04%和0.941,相比小波和SVR模型来说具有较高的预测精度,同时叠加预测误差较小,其MAPE为7.657%,EC为0.894;而在长期预测时,该模型预测精度不如SVR模型;同时Volterra模型对混沌特征弱的月降水量进行短期预测时,其模型预测效果并不理想,MAPE为54.855%,EC仅为0.566。【结论】该方法能提供精确度较高的降水量预测值,为降水量的预测提供一种新的方法。【Objective】The time series of rainfall is a nonlinear, non-stationary process and can be analyzed statistically. The purpose of this paper is to analyze the chaotic characteristics of rainfalls in attempts to develop a chaotic model to predict monthly precipitation. 【Method 】We took monthly precipitation measured from the weather station at Bomi between Linzhi and Bomi on the G318 highway as an example, the C-C correlation integral method was used to determine the delay time τ and the embedding dimension m in it. The time series was then reconstructed in phase space whose Lyapunov exponent was obtained for a small sub-dataset to determine the chaotic characteristics, from which we constructed a Volterra model to predict monthly rainfall in both short-term(5 years) and long-term(15 years) respectively. The predicted monthly rainfalls using the proposed model were compared with those predicted by the wavelet model and the SVR prediction model.【Result】The MAPE and EC of the rainfalls predicted using the proposed Volterra model for short-term was 4.04% and 0.941 respectively.Compared with the wavelet and SVR model, the proposed Volterra model was more accurate, and its superposition prediction error was smaller, with its associated MAPE and EC being 7.657% and 0.894 respectively. However, the rainfalls predicted by the proposed for long-term were not as good as those by the SVR model. When the time series of the rainfall was less chaotic, the prediction of Volterra model for short-term rainfall was less reliable, with its associated MAPE and EC being 54.855% and 0.566, respectively.【Conclusion】The chaotic model was more accurate than the traditional model for predicting monthly rainfall only for short-term and when the time series of the rainfalls is chaotic. Therefore, it should be used with care.

关 键 词：混沌理论 相空间重构 LYAPUNOV指数 VOLTERRA滤波器 降水量预测 

分 类 号：P457[天文地球—大气科学及气象学] P208