An Earthquake Prediction System for Bangladesh Using Deep Long Short-Term Memory Architecture

2021 ◽  
pp. 465-476
Author(s):  
Md. Hasan Al Banna ◽  
Tapotosh Ghosh ◽  
Kazi Abu Taher ◽  
M. Shamim Kaiser ◽  
Mufti Mahmud
Keyword(s):  
Earthquake Prediction ◽  
Short Term Memory ◽  
Prediction System ◽  
Memory Architecture ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Development of a daily PM<sub>10</sub> and PM<sub>2.5</sub> prediction system using a deep long short-term memory neural network model

2019 ◽  
Vol 19 (20) ◽  
pp. 12935-12951 ◽  
Author(s):  
Hyun S. Kim ◽  
Inyoung Park ◽  
Chul H. Song ◽  
Kyunghwa Lee ◽  
Jae W. Yun ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Transport Model ◽  
Initial Conditions ◽  
Current System ◽  
Prediction System ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

Abstract. A deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. The structural and learnable parameters of the newly developed system were optimized from iterative model training. Independent variables were obtained from ground-based observations over 2.3 years. The performance of the particulate matter (PM) prediction LSTM was then evaluated by comparisons with ground PM observations and with the PM concentrations predicted from two sets of 3-D chemistry-transport model (CTM) simulations (with and without data assimilation for initial conditions). The comparisons showed, in general, better performance with the LSTM than with the 3-D CTM simulations. For example, in terms of IOAs (index of agreements), the PM prediction IOAs were enhanced from 0.36–0.78 with the 3-D CTM simulations to 0.62–0.79 with the LSTM-based model. The deep LSTM-based PM prediction system developed at observation sites is expected to be further integrated with 3-D CTM-based prediction systems in the future. In addition to this, further possible applications of the deep LSTM-based system are discussed, together with some limitations of the current system.

PREDICTION OF STOCK PRICES VIA RECURRENT NEURAL NETWORKS WITH LSTM (LONG SHORT-TERM MEMORY) ARCHITECTURE

2020 ◽  
Vol 128 (1) ◽  
pp. 53-65
Author(s):  
Hyacinthe Kouassi Konan ◽  
Francis Adlès Kouassi ◽  
Mamadou Coulibaly ◽  
Olivier Asseu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Stock Prices ◽  
Short Term Memory ◽  
Memory Architecture ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2826 ◽  
Author(s):  
Ma ◽  
Wu ◽  
Zhang ◽  
Wu ◽  
Jeon ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Good Effect ◽  
Prediction System ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Term Variation ◽  
Long Short Term Memory

In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

scholarly journals Attention-based Bi-directional Long-Short Term Memory Network for Earthquake Prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Md. Hasan Al Banna ◽  
Tapotosh Ghosh ◽  
Md. Jaber Al Nahian ◽  
Kazi Abu Taher ◽  
M. Shamim Kaiser ◽  
...  
Keyword(s):  
Earthquake Prediction ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Regional soil moisture prediction system based on Long Short-Term Memory network

2022 ◽  
Vol 213 ◽  
pp. 30-38
Author(s):  
Nemanja Filipović ◽  
Sanja Brdar ◽  
Gordan Mimić ◽  
Oskar Marko ◽  
Vladimir Crnojević
Keyword(s):  
Soil Moisture ◽  
Short Term Memory ◽  
Prediction System ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Development of daily PM<sub>10</sub> and PM<sub>2.5</sub> prediction system using a deep long short-term memory neural network model

2019 ◽  
Author(s):  
Hyun S. Kim ◽  
Inyoung Park ◽  
Chul H. Song ◽  
Kyunghwa Lee ◽  
Jae W. Yun ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Transport Model ◽  
Initial Conditions ◽  
Current System ◽  
Prediction System ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

Abstract. A deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. The structural and learnable parameters of the newly developed system were optimized from iterative model trainings. Independent variables were obtained from ground-based observations over 2.3 years. The performance of the particulate matter (PM) prediction LSTM was then evaluated by comparisons with ground PM observations and with the PM concentrations predicted from two sets of 3-D chemistry-transport model (CTM) simulations (with and without data assimilation for initial conditions). The comparisons showed, in general, better performance with the LSTM than with the 3-D CTM simulations. For example, in terms of IOAs (index of agreements), the PM prediction IOAs were enhanced from 0.36–0.78 with the 3-D CTM simulations to 0.62–0.79 with the LSTM-based model. The deep LSTM-based PM prediction system developed at observation sites is expected to be further integrated with 3-D CTM-based prediction systems in the future. In addition to this, further possible applications of the deep LSTM-based system are discussed, together with some limitations of the current system.

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