scholarly journals Ensemble streamflow forecasting based on variational mode decomposition and long short term memory

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaomei Sun ◽  
Haiou Zhang ◽  
Jian Wang ◽  
Chendi Shi ◽  
Dongwen Hua ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Historical Changes ◽  
Daily Streamflow ◽  
Mode Decomposition ◽  
Long Short Term Memory

AbstractReliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE = 36.3692), determination coefficient (R2 = 0.9890), mean absolute error (MAE = 9.5246) and peak percentage threshold statistics (PPTS(5) = 0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

scholarly journals Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting

2021 ◽  
Author(s):  
Yani Lian ◽  
Jungang Luo ◽  
Jingmin Wang ◽  
Ganggang Zuo
Keyword(s):  
Climate Change ◽  
Short Term Memory ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Daily Streamflow ◽  
Runoff Forecasting ◽  
Long Short Term Memory

Abstract Many previous studies have developed decomposition and ensemble models to improve runoff forecasting performance. However, these decomposition-based models usually introduce large decomposition errors into the modeling process. Since the variation in runoff time series is greatly driven by climate change, many previous studies considering climate change focused on only rainfall-runoff modeling, with few meteorological factors as input. Therefore, a climate-driven streamflow forecasting (CDSF) framework was proposed to improve the runoff forecasting accuracy. This framework is realized using principal component analysis (PCA), long short-term memory (LSTM) and Bayesian optimization (BO) referred to as PCA-LSTM-BO. To validate the effectiveness and superiority of the PCA-LSTM-BO method with which one autoregressive LSTM model and two other CDSF models based on PCA, BO, and either support vector regression (SVR) or, gradient boosting regression trees (GBRT), namely, PCA-SVR-BO and PCA-GBRT-BO, respectively, were compared. A generalization performance index based on the Nash-Sutcliffe efficiency (NSE), called the GI(NSE) value, is proposed to evaluate the generalizability of the model. The results show that (1) the proposed model is significantly better than the other benchmark models in terms of the mean square error (MSE<=185.782), NSE>=0.819, and GI(NSE) <=0.223 for all the forecasting scenarios; (2) the PCA in the CDSF framework can improve the forecasting capacity and generalizability; (3) the CDSF framework is superior to the autoregressive LSTM models for all the forecasting scenarios; and (4) the GI(NSE) value is demonstrated to be effective in selecting the optimal model with a better generalizability.

scholarly journals Streamflow forecasting based on the hybrid decomposition-ensemble model 

2021 ◽  
Author(s):  
Xiaomei Sun ◽  
Haiou Zhang ◽  
Jian Wang ◽  
Chendi Shi ◽  
Dongwen Hua ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Model ◽  
Streamflow Forecasting ◽  
Historical Changes ◽  
Daily Streamflow ◽  
Mode Decomposition ◽  
Ensemble Strategy ◽  
Hybrid Decomposition

Abstract Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) Feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE=36.3692), determination coefficient (R 2 =0.9890), mean absolute error (MAE=9.5246) and peak percentage threshold statistics (PPTS(5)=0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

scholarly journals A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qichun Bing ◽  
Fuxin Shen ◽  
Xiufeng Chen ◽  
Weijian Zhang ◽  
Yanran Hu ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Hybrid Model ◽  
Short Term Memory ◽  
Prediction Method ◽  
Prediction Performance ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Long Short Term Memory

Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance.

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