scholarly journals Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks

2016 ◽  
Vol 51 (4) ◽  
pp. 149-161
Author(s):  
Yu Lei ◽  
Danning Zhao ◽  
Hongbing Cai
Keyword(s):  
Neural Networks ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Short Term ◽  
Pass Filter ◽  
Term Prediction ◽  
Mode Decomposition ◽  
Short Period ◽  
The Future ◽  
Short Term Prediction

Abstract It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.

Short-term prediction of UT1-UTC by combination of the grey model and neural networks

2017 ◽  
Vol 59 (2) ◽  
pp. 524-531 ◽  
Author(s):  
Yu Lei ◽  
Min Guo ◽  
Dan-dan Hu ◽  
Hong-bing Cai ◽  
Dan-ning Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Grey Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Very short-term prediction of torrential rains using polarimetric phased-array radar (MP-PAWR) and deep neural networks

2021 ◽  
Author(s):  
Philippe Baron ◽  
Hiroshi Hanado ◽  
Dong-Kyun Kim ◽  
Seiji Kawamura ◽  
Takeshi Maesaka ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Phased Array ◽  
Short Term ◽  
Term Prediction ◽  
Phased Array Radar ◽  
Short Term Prediction

scholarly journals A new Lagrangian-based short-term prediction methodology for high-frequency (HF) radar currents

Ocean Science ◽  
2021 ◽  
Vol 17 (3) ◽  
pp. 755-768
Author(s):  
Lohitzune Solabarrieta ◽  
Ismael Hernández-Carrasco ◽  
Anna Rubio ◽  
Michael Campbell ◽  
Ganix Esnaola ◽  
...  
Keyword(s):  
Real Time ◽  
High Frequency ◽  
Skill Score ◽  
Hf Radar ◽  
Surface Currents ◽  
Short Term ◽  
High Frequency Radar ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Temporal Horizon

Abstract. The use of high-frequency radar (HFR) data is increasing worldwide for different applications in the field of operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian-based, empirical, real-time, short-term prediction (L-STP) system is presented in order to provide short-term forecasts of up to 48 h of ocean currents. The method is based on finding historical analogs of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as the historical analog, we perform the forecast. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. A comparison of the L-STP methodology with predictions based on persistence and reference fields is performed in order to quantify the error introduced by this approach. Furthermore, a sensitivity analysis has been conducted to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill score has been developed using the results of this analysis, which allows for the identification of periods when the short-term prediction performance is more likely to be low, and persistence can be used as a better predictor for the future currents.

scholarly journals A Short-Term Prediction Model of PM2.5 Concentration Based on Deep Learning and Mode Decomposition Methods

2021 ◽  
Vol 11 (15) ◽  
pp. 6915
Author(s):  
Jun Wei ◽  
Fan Yang ◽  
Xiao-Chen Ren ◽  
Silin Zou
Keyword(s):  
Neural Networks ◽  
Low Frequency ◽  
Decomposition Methods ◽  
Prediction Errors ◽  
Beijing City ◽  
Short Term ◽  
Mode Decomposition ◽  
Short Term Prediction ◽  
Bp Model ◽  
Pm2.5 Concentration

Based on a set of deep learning and mode decomposition methods, a short-term prediction model for PM2.5 concentration for Beijing city is established in this paper. An ensemble empirical mode decomposition (EEMD) algorithm is first used to decompose the original PM2.5 timeseries to several high- to low-frequency intrinsic mode functions (IMFs). Each IMF component is then trained and predicted by a combination of three neural networks: back propagation network (BP), long short-term memory network (LSTM), and a hybrid network of a convolutional neural network (CNN) + LSTM. The results showed that both BP and LSTM are able to fit the low-frequency IMFs very well, and the total prediction errors of the summation of all IMFs are remarkably reduced from 21 g/m3 in the single BP model to 4.8 g/m3 in the EEMD + BP model. Spatial information from 143 stations surrounding Beijing city is extracted by CNN, which is then used to train the CNN+LSTM. It is found that, under extreme weather conditions of PM2.5 <35 g/m3 and PM2.5 >150 g/m3, the prediction errors of the CNN + LSTM model are improved by ~30% compared to the single LSTM model. However, the prediction of the very high-frequency IMF mode (IMF-1) remains a challenge for all neural networks, which might be due to microphysical turbulences and chaotic processes that cannot be resolved by the above-mentioned neural networks based on variable–variable relationship.

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