| 引用本文: | 舒涛,路昊天,曹景轩, 等.基于混沌理论的降水量预测方法研究[J].灌溉排水学报,2022,41(3):83-91. |
| SHU Tao,LU Haotian,CAO Jingxuan, et al..基于混沌理论的降水量预测方法研究[J].灌溉排水学报,2022,41(3):83-91. |
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| 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t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5 a和长期15 a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。 |
| 关键词: 混沌理论；相空间重构；Lyapunov指数；Volterra滤波器；降水量预测 |
| DOI：10.13522/j.cnki.ggps.2021385 |
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| Predicting Monthly Precipitation Using Chaotic Model |
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SHU Tao, LU Haotian, CAO Jingxuan, et al.
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1.College of Marine Geosciences, Ocean University of China, Qingdao 266100, China;
2. College of Earth Sciences, Guilin University of Technology, Guilin 541004, China;
3.College of Engineering, Tibet University, Lhasa 850000, China; 4. Department of Construction Engineering,
Dalian University of Technology, Dalian 116024, China; 5. Key Laboratory of Mountain Hazards and Earth Surface Process,
Chinese Academy of Sciences, Chengdu 610041, China; 6. Institute of Mountain Hazards and Environment,
Chinese Academy of Sciences ＆ Ministry of Water Conservancy, Chengdu 610041, China;
7.University of Chinese Academy of Sciences, Beijing 100049, China
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| Abstract: |
| 【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 t 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. |
| Key words: chaotic model; phase space reconstruction; lyapunov index; volterra filter; rainfall prediction |
