LSTM-based earthquake prediction: enhanced time feature and data representation

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

doi:10.1504/ijhpsa.2021.115499

Cited by 8 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kavianpour et al [29] introduced a novel prediction model based on the attention mechanism, Convolution Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) models, to capture the temporal dependencies, which can predict the maximum MW and the number of earthquakes in a specified period in mainland China. Berhich et al [4] calculated an appropriate feature to enhance time feature and predict MW based on LSTM with this feature. Nevertheless, most of these DL methods didn't consider some precursor features are irrelevant features, even redundant features.…”

Section: Earthquake Prediction Methodsmentioning

confidence: 99%

“…These operations are described as Eqs. (4)(5)(6)(7)(8)(9). 1 shows the partial earthquake data of DS2 after merging.…”

Section: The Lstm For Mw Predictionmentioning

confidence: 99%

“…Crustal movement is a continuous process makes MW prediction to be a time-series issue. Hence, in [4], Berhich et al calculated an appropriate feature by historical data to enhance time-series task and implemented LSTM to predict MW with the enhanced features. Cai et al [5] used three groups of real seismic data: gravity, georesistivity, and water-level dataset to predict MW based on LSTM.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Elite GA-based Feature Selection of LSTM for Earthquake Prediction

Lan

Wen

et al. 2023

Preprint

View full text Add to dashboard Cite

Earthquake magnitude prediction is an extremely difficult task that has been studied by various machine learning researchers. However, the redundant features and time series properties hinder the development of prediction models. Elite Genetic Algorithm (EGA) has the advantages in searching optimal feature subsets, meanwhile, Long Short-Term Memory (LSTM) is dedicated to processing time series and complex data. Therefore, we propose an EGA-based feature selection of LSTM model (EGA-LSTM) for time series earthquake prediction. First, the acoustic and electromagnetics data of the AETA system we developed are fused and preprocessed by EGA, aiming to find strong correlation indicators. Second, LSTM is introduced to execute magnitude prediction with the selected features. Specifically, the RMSE of LSTM and the ratio of selected features are chosen as fitness components of EGA. Finally, we test the proposed EGA-LSTM on the AETA data of Sichuan province, including the influence of data in different periods ($timePeriod$) and fitness function weights ($\omega_a$ and $\omega_F$) on the prediction results. Linear Regression (LR), Support Vector Regression (SVR), Adaboost, Random Forest (RF), standard GA (SGA), steadyGA, and three Differential Evolution Algorithms (DEs) are adopted as our baselines. Experimental results demonstrate that all the methods can get the best performance when $timePeriod = 0:00-8:00$, $\omega_a=1$, and $\omega_F=0.8$. Moreover, our proposed approach is superior to state-of-the-art approaches on the evaluation indicators MAE, MSE, RMSE, and $R_2$. Non-parametric tests reveal that EGA-LSTM is significantly different from others and outperforms the standard LSTM.

show abstract

Section: Earthquake Prediction Methodsmentioning

confidence: 99%

“…These operations are described as Eqs. (4)(5)(6)(7)(8)(9). 1 shows the partial earthquake data of DS2 after merging.…”

Section: The Lstm For Mw Predictionmentioning

confidence: 99%