scholarly journals Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1452
Author(s):  
Xuchu Jiang ◽  
Peiyao Wei ◽  
Yiwen Luo ◽  
Ying Li
Keyword(s):  
Short Term Memory ◽  
Prediction Method ◽  
Input Sequence ◽  
Absolute Error ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Air Pollutant ◽  
Percentage Error ◽  
Mode Decomposition ◽  
Concentration Prediction

The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The “CEEMDAN-FE” decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model’s performance in predicting the real-time concentrations of PM2.5.

scholarly journals Coal Thickness Prediction Method Based on VMD and LSTM

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 232
Author(s):  
Yaping Huang ◽  
Lei Yan ◽  
Yan Cheng ◽  
Xuemei Qi ◽  
Zhixiong Li
Keyword(s):  
Neural Network ◽  
Coal Seam ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Prediction Method ◽  
Absolute Error ◽  
High Accuracy ◽  
Mine Safety ◽  
Mode Decomposition ◽  
Thickness Prediction

The change in coal seam thickness has an important influence on coal mine safety and efficient mining. It is very important to predict coal thickness accurately. However, the accuracy of borehole interpolation and BP neural network is not high. Variational mode decomposition (VMD) has strong denoising ability, and the long short-term memory neural network (LSTM) is especially suitable for the prediction of complex sequences. This paper presents a method of coal thickness prediction using VMD and LSTM. Firstly, empirical mode decomposition (EMD) and VMD methods are used to denoise simple signals, and the denoising effect of the VMD method is verified. Then, the wedge-shaped coal thickness model is constructed, and the seismic forward modeling and analysis are carried out. The results show that the coal thickness prediction based on seismic attributes is feasible. On the basis of VMD denoising of the original 3D seismic data, VMD-LSTM is used to predict coal thickness and compared with the prediction results of the traditional BP neural network. The coal thickness prediction method proposed in this paper has high accuracy and basically coincides with the coal seam information exposed by existing boreholes. The minimum absolute error of the predicted coal thickness is only 0.08 m, and the maximum absolute error is 0.48 m. This indicates that VMD-LSTM has high accuracy in predicting coal thickness. The proposed coal thickness prediction method can be used as a new method for coal thickness prediction.

scholarly journals A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Baobin Zhou ◽  
Che Liu ◽  
Jianjing Li ◽  
Bo Sun ◽  
Jun Yang ◽  
...  
Keyword(s):  
Wind Power ◽  
Hybrid Method ◽  
Short Term Memory ◽  
Moving Average ◽  
Principal Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Power Prediction ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Meteorological Features

High-precision wind power prediction is important for the planning, economics, and security maintenance of a power grid. Meteorological features and seasonal information are strongly related to wind power prediction. This paper proposes a hybrid method for ultrashort-term wind power prediction considering meteorological features (wind direction, wind speed, temperature, atmospheric pressure, and humidity) and seasonal information. The wind power data are decomposed into stationary subsequences using the ensemble empirical mode decomposition (EEMD). The principal component analysis (PCA) is used to reduce the redundant meteorological features and the algorithm complexity. With the stationary subsequences and extracted meteorological features data as inputs, the long short-term memory (LSTM) network is used to complete the wind power prediction. Finally, the seasonal autoregressive integrated moving average (SARIMA) is innovatively used to fit seasonal features (quarterly and monthly) of wind power and reconstruct the prediction results of LSTM. The proposed method is used to predict 15-minute wind power. In this study, three datasets were collected from a windfarm in Laizhou to validate the prediction performance of the proposed method. The experimental results showed that the prediction accuracy was significantly improved when meteorological features were considered and further improved with seasonal correction.

scholarly journals Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Peng-fei Zheng ◽  
Jun Wang
Keyword(s):  
Fault Diagnosis ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Coupling System ◽  
Reconstructed Signal ◽  
Gear Transmission System ◽  
Gear Fault ◽  
Diagnosis Method

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

scholarly journals Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1942
Author(s):  
Pyae Pyae Phyo ◽  
Yung-Cheol Byun
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Absolute Error ◽  
Percentage Error ◽  
Ensemble Model ◽  
Load Prediction ◽  
Energy Prediction ◽  
The Times ◽  
One Year

The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

scholarly journals RNN-based Dimensional Speech Emotion Recognition

2020 ◽  
Author(s):  
Bagus Tris Atmaja
Keyword(s):  
Emotion Recognition ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Percentage Error ◽  
Concordance Correlation ◽  
Acoustic Feature ◽  
Dense System

◆ A speech emotion recognition system based on recurrent neural networks is developed using long short-term memory networks.◆ Two of acoustic feature sets are evaluated: 31 Features (3 time-domain features, 5 frequency-domain features, 13 MFCCs, 5 F0s, and 5 Harmonics) and eGeMaps feature set (23 features).◆ To evaluate the performance, some metrics are used i.e. mean squared error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and concordance correlation coefficient (CCC). Among those metrics, CCC is main focus as it is used by other researchers.◆ The developed system used multi-task learning to maximize arousal, valence, and dominance at the same time using CCC loss (1 - CCC). The result shows using LSTM networks improve the CCC score compared to baseline dense system. The best CCC score isobtained on arousal followed by dominance and valence.

scholarly journals Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM

2020 ◽  
Vol 12 (6) ◽  
pp. 2451 ◽  
Author(s):  
Hualing Lin ◽  
Qiubi Sun ◽  
Sheng-Qun Chen
Keyword(s):  
Time Series ◽  
International Trade ◽  
Exchange Rate ◽  
Empirical Mode Decomposition ◽  
Foreign Currency ◽  
Short Term Memory ◽  
Objective Evaluation ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Mixing Problem

In international trade, it is common practice for multinational companies to use financial market instruments, such as financial derivatives and foreign currency debt, to hedge exchange rate risks. Making accurate predictions and decisions on the direction and magnitude of exchange rate movements is a more direct way to reduce exchange rate risks. However, the traditional time series model has many limitations in forecasting exchange rate, which is nonlinear and nonstationary. In this paper, we propose a new hybrid model of complete ensemble empirical mode decomposition (CEEMDAN) based multilayer long short-term memory (MLSTM) networks. It overcomes the shortcomings of the classic methods. CEEMDAN not only solves the mode mixing problem of empirical mode decomposition (EMD), but also solves the residue noise problem which is included in the reconstructed data of ensemble empirical mode decomposition (EEMD) with less computation cost. MLSTM can learning more complex dependences from exchange rate data than the classic model of time series. A lot of experiments have been conducted to measure the performance of the proposed approach among the exchange rates of British pound, the Australian dollar, and the US dollar. In order to get an objective evaluation, we compared the proposed method with several standard approaches or other hybrid models. The experimental results show that the CEEMDAN-based MLSTM (CEEMDAN–MLSTM) goes on better than some state-of-the-art models in terms of several evaluations.

Epilepsy Seizure Detection Using Akima Spline Interpolation Based Ensemble Empirical Mode Kalman Filter Decomposition by EEG Signals

2019 ◽  
Vol 9 (6) ◽  
pp. 1320-1328
Author(s):  
K. Baskar ◽  
C. Karthikeyan
Keyword(s):  
Kalman Filter ◽  
Short Term Memory ◽  
Spline Interpolation ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Sets ◽  
Eeg Signals ◽  
Stable Algorithm ◽  
Learning Classifier ◽  
Mode Decomposition

Epilepsy is an incessant neurological disorder. The Epilepsy seizures are generated due to the aggravation in transient signals in Cerebrum. These seizures can be detected by analyzing the Electroencephalogram (EEG) Signals. The Akima Spline Interpolation based Ensemble Empirical Mode Kalman Filter Decomposition (ASI-EEMKFD) model proposed in the paper focuses on detecting seizures automatically through a stable algorithm written in Python by using PyEEG package. The signal detection process is done in three phases. First, the EEG signals are acquired through data sets. Then the signal is decomposed using Akima Spline interpolation for finding the intrinsic mode function. Further the signal is decomposed by implementing the steps involved in the Ensemble Empirical Mode Decomposition (EEMD). During the decomposition Kalman filter is used in order to remove the white Gaussian noise. Finally, the decomposed signals are applied to the Long Term Short Term Memory (LTST) deep learning classifier which classifies the ictal, pre-ictal and healthy signal. Our proposed method produces the result higher compared with the existing EEMD Methods with the accuracy rate of 98.2%, sensitivity of 94.96% and specificity of 93.72%.

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