An attention-based LSTM network for large earthquake prediction

Berhich, Asmae; Belouadha, Fatima-Zahra; Kabbaj, Mohammed Issam

doi:10.1016/j.soildyn.2022.107663

Cited by 23 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To accurately evaluate the rationality of the network input set, this study uses equation ( 16) to calculate the Pearson correlation coefficient between the input characteristics [34], such as the peak value of the second harmonics and the concentration; the specific calculation results are shown in figure 11…”

Section: Correlation Analysis Of Input Featuresmentioning

confidence: 99%

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

Wang,

Chen,

Kong

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

In order to improve the detection accuracy of CO2 and other gases in the flue gas emitted from thermal power plants, a CO2 concentration detection model based on tunable semiconductor laser absorption spectroscopy was proposed. First, a variational mode decomposition model was used to filter the harmonic signal after removing the outliers to reduce the influence of noise on the detection results. Suitable absorption lines and concentration characteristics were then selected according to the gas absorption properties and correlation theory. Finally, the CO2 concentration inversion was completed using long short-term memory networks, and a Bayesian optimization algorithm was introduced to optimize the hyperparameters of the network. The experimental results showed that the R 2 and RMSE of the test set were 0.998 84 and 0.116 08, respectively, in the concentration range of 1%–12%. In addition, the Allan analysis of variance revealed that the maximum measurement error of CO2 was only 0.005 619% when the integration time was 38 s. Compared to the traditional CO2 detection schemes, the detection accuracy and stability are significantly improved, which provides a feasible scheme for flue gas detection in thermal power plants.

show abstract

Section: Correlation Analysis Of Input Featuresmentioning

confidence: 99%

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

Wang,

Chen,

Kong

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The LSTM network is an RNN variant that addresses vanishing gradients or outburst issues by switching memory cells for hidden nodes in the RNN architecture. Selective state alteration is made feasible by the network through the configuration of three control gate units: input, output, and forget (Berhich et al, 2023). The dependency information is established by the input gate, unworthy data is discarded by the forget gate, and stored information is sent to the next neuron by the output gate (Xin et al, 2024).…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Short-term wind power forecasting using integrated boosting approach

Ahmed,

Muhammad,

Abbas

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

Rapidly increasing global energy demand and environmental concerns have shifted the attention of policymakers toward the large-scale integration of renewable energy resources (RERs). Wind energy is a type of RERs with vast energy potential and no environmental pollution is associated with it. The sustainable development goals: affordable and clean energy, climate action, and industry, innovation and infrastructure, can be achieved by integrating wind energy into the existing power systems. However, the integration of wind energy will bring instability challenges due to its intermittent nature. Mitigating these challenges necessitates the implementation of effective wind power forecasting models. Therefore, we have proposed a novel integrated approach, Boost-LR, for hour-ahead wind power forecasting. The Boost-LR is a multilevel technique consisting of non-parametric models, extreme gradient boosting (XgBoost), categorical boosting (CatBoost), and random forest (RF), and parametric approach, linear regression (LR). The first layer of the Boost-LR uses the boosting algorithms that process the data according to their tree development architectures and pass their intermediary forecast to LR which is deployed in layer two and processes the intermediary forecasts of layer one models to provide the final predicted wind power. To demonstrate the generalizability and robustness of the proposed study, the performance of Boost-LR is compared with the individual models of CatBoost, XgBoost, RF, deep learning networks: long short-term memory (LSTM) and gated recurrent unit (GRU), Transformer and Informer models using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and normalized root mean square error (NRMSE). Findings demonstrate the effectiveness of the Boost-LR as its forecasting performance is superior to the compared models. The improvement in MAE of Boost-LR is recorded as to be 31.42%, 32.14%, and 27.55% for the datasets of Bruska, Jelinak, and Inland wind farm, respectively as compared to the MAE of CatBoost which is revealed as the second-best performing model. Moreover, the proposed study also reports a literature comparison that further validates the effectiveness of Boost-LR performance for short-term wind power forecasting.

show abstract

“…where Y î expresses the predicted value, Y i is the observed value, and n is the number of samples. The MAE scores are linearly increasing with the increase in errors [31,32]. ACCscore (Accuracy score) To evaluate the accuracy of the numerical data, we applied an allowable error range based on the distribution of each datum.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Deep-Learning-Based Natural Ventilation Rate Prediction with Auxiliary Data in Mismeasurement Sensing Environments

Yang,

Kim,

Lee

2023

Electronics

View full text Add to dashboard Cite

Predicting the amount of natural ventilation by utilizing environmental data such as differential pressure, wind, temperature, and humidity with IoT sensing is an important issue for optimal HVAC control to maintain comfortable air quality. Recently, some research has been conducted using deep learning to provide high accuracy in natural ventilation prediction. Therefore, high reliability of IoT sensing data is required to achieve predictions successfully. However, it is practically difficult to predict the accurate NVR in a mismeasurement sensing environment, since inaccurate IoT sensing data are collected, for example, due to sensor malfunction. Therefore, we need a way to provide high deep-learning-based NVR prediction accuracy in mismeasurement sensing environments. In this study, to overcome the degradation of accuracy due to mismeasurement, we use complementary auxiliary data generated by semi-supervised learning and selected by importance analysis. That is, the NVR prediction model is reliably trained by generating and selecting auxiliary data, and then the natural ventilation is predicted with the integration of mismeasurement and auxiliary by bagging-based ensemble approach. Based on the experimental results, we confirmed that the proposed method improved the natural ventilation rate prediction accuracy by 25% compared with the baseline approach. In the context of deep-learning-based natural ventilation prediction using various IoT sensing data, we address the issue of realistic mismeasurement by generating auxiliary data that utilize the rapidly changing or slowly changing characteristics of the sensing data, which can improve the reliability of observation data.

show abstract

An attention-based LSTM network for large earthquake prediction

Cited by 23 publications

References 22 publications

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

Short-term wind power forecasting using integrated boosting approach

Deep-Learning-Based Natural Ventilation Rate Prediction with Auxiliary Data in Mismeasurement Sensing Environments

Contact Info

Product

Resources

About

An attention-based LSTM network for large earthquake prediction

Cited by 23 publications

References 22 publications

High-precision concentration detection of CO2 in flue gas based on BO-LSTM and variational mode decomposition

High-precision concentration detection of CO2 in flue gas based on BO-LSTM and variational mode decomposition

Short-term wind power forecasting using integrated boosting approach

Deep-Learning-Based Natural Ventilation Rate Prediction with Auxiliary Data in Mismeasurement Sensing Environments

Contact Info

Product

Resources

About

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition

High-precision concentration detection of CO₂ in flue gas based on BO-LSTM and variational mode decomposition