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	基于时频分析的LSTM组合模型径流预测
其他题名	Runoff prediction with LSTM-based combination model on time-frequency analysis
	蔡文静; 陈伏龙; 何朝飞; 骆成彦; 龙爱华
来源期刊	干旱区地理
ISSN	1000-6060
出版年	2021
卷号	44 期号:6 页码:1696-1706
中文摘要	针对变化环境下径流时间序列复杂的非线性、非平稳性特征,为提高中长期径流预测的准确性,运用多种时频分析方法构建组合预报模型以探究适用性。以干旱区典型内陆河玛纳斯河为例,利用经验模态分解(EMD)、变分模态分解(VMD)、离散小波变换(DWT)时频分析方法对径流时间序列进行多尺度分解,得到不同频率和特征的子序列。以前期径流、降水量、气温、大气环流因子等作为长短期记忆神经网络模型(LSTM)的输入变量,采用随机森林法和Pearson相关系数法确定各子序列的最佳预报因子,基于时频分析方法分别构建EMD-LSTM、VMD-LSTM、DWT-LSTM组合预报模型,通过LSTM模型对各子序列进行预测,加和重构获得最终预测结果,并与单一的误差反向传播神经网络(BP)、极限学习机(ELM)、LSTM模型的预测结果进行对比分析。结果表明:组合模型VMD-LSTM预报误差最小、精度最高,纳什系数保持在0.9以上,有效避免了过拟合等问题,其径流极值预测误差在15%以内,对径流总体趋势预测和极值的追踪均有良好效果。研究结果可为流域水资源规划与调度提供参考。
英文摘要	This study investigated the complex nonlinear and nonstationary characteristics of runoff time series in a changing environment. We obtained accurate and reliable runoff prediction results by constructing long-short term memory (LSTM) combination models on the basis of time frequency analysis methods. The combination models were then applied to the Manas River, Xinjiang, China, a typical inland river in an arid area. Empirical mode decomposition (EMD), variational mode decomposition (VMD), and discrete wavelet transform (DWT) were first applied to decompose the original runoff series into several subsequences with different frequencies and characteristics. Second, we used the previous runoff, precipitation, temperature, and atmospheric circulation factors as the input variables of the LSTM model. At the same time, the optimal predictor of each subsequence was determined according to random forest classification and the Pearson correlation coefficient. Finally, the combined models were based on a combination of VMD, EMD, and DWT with LSTM. Accordingly, they were called VMD-LSTM, EMD-LSTM, and DWT-LSTM and are proposed and applied for runoff forecasting. The total output of all submodules was treated as the final forecasting result for the original runoff. A single back propagation neural network, a single extreme learning machine, and a single LSTM were adopted as comparative forecast models. The results indicated that the VMD-LSTM model had the best forecasting performance among all the models in terms of its Nash-Sutcliffe error (NSE=0.930), root mean square error (RMSE=0.385), and coefficient of determination (R~2=0.940). The extreme value prediction error for the runoff of the VMD-LSTM model was within 15%, and it had a good effect on the overall trend prediction and extreme value tracking regarding the runoff. This result further verified the accuracy and stability of the VMD-LSTM model. On the basis of the above results, the accuracy and stability of the VMD-LSTM model were further verified. Thus, on the basis of sequence decomposition, considering the influence of predictors on subsequences can help promote accurate and stable prediction results. These research results will provide a reference for river basin water resource planning and dispatching.
中文关键词	径流预测 ; 组合模型 ; 时频分析 ; 长短期记忆神经网络
英文关键词	runoff prediction combination model time-frequency analysis long short-term memory
类型	Article
语种	中文
收录类别	CSCD
WOS类目	Geology
CSCD记录号	CSCD:7114548
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/377652
作者单位	蔡文静, 石河子大学水利建筑工程学院, 石河子, 新疆 832000, 中国.; 陈伏龙, 石河子大学水利建筑工程学院, 石河子, 新疆 832000, 中国.; 何朝飞, 石河子大学水利建筑工程学院, 石河子, 新疆 832000, 中国.; 骆成彦, 石河子大学水利建筑工程学院, 石河子, 新疆 832000, 中国.; 龙爱华, 石河子大学水利建筑工程学院;;中国水利水电科学研究院, ;;流域水循环模拟与调控国家重点实验室, 石河子;;, 新疆;;北京 832000;;100038, 中国.
推荐引用方式 GB/T 7714	蔡文静,陈伏龙,何朝飞,等. 基于时频分析的LSTM组合模型径流预测[J],2021,44(6):1696-1706.
APA	蔡文静,陈伏龙,何朝飞,骆成彦,&龙爱华.(2021).基于时频分析的LSTM组合模型径流预测.干旱区地理,44(6),1696-1706.
MLA	蔡文静,et al."基于时频分析的LSTM组合模型径流预测".干旱区地理 44.6(2021):1696-1706.