Duration prediction of Chilean strong motion data using machine learning

Chanda, Sarit; Raghucharan, M. C.; Reddy, K. S. K. Karthik; Chaudhari, Vasudeo; Somala, S.

doi:10.1016/j.jsames.2021.103253

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…Through deep learning, the extension of ML technique, which is considered the effective prediction process; and its error rates are low when compared with other ML models [14]. Moreover, it allows concerned people to detect risk before happening, as used in the healthcare area, it shows an undertaking results to consider when dealing with patients [15] and in its application field in earth science shows prominent results to predict the accuracy of a variable [16]. However, existing prediction models do not sufficiently take user behavior into account [17].…”

Section: Results Predictionmentioning

confidence: 99%

Machine Learning for sport results prediction using algorithms

Lotfi

rebbouj

2021

IJITAS

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This paper describes the use of machine learning in sports. Given the recent trend in Data science and sport analytics, the use of Machine Learning and Data Mining as techniques in sport reveals the essential contribution of technology in results and performance prediction. The purpose of this paper is to benchmark existing analysis methods used in literature, to understand the prediction processes used to model Data collection and its analysis; and determine the characteristics of the variables controlling the performance. Finally, this paper will suggest the reliable tool for Data mining analysis technique using Machine Learning.

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Section: Results Predictionmentioning

confidence: 99%

Machine Learning for sport results prediction using algorithms

Lotfi

rebbouj

2021

IJITAS

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“…These techniques show significant and encouraging results but they were still not available so far. As Chile is rocked by intraplate, interface, and crustal events, Chanda et al [14] proposed six different ML methods to predict the total duration and significant duration for the three types of earthquakes. Shah et al [15] introduced an Improved Artificial Bee Colony algorithm to improve the training process of Multilayer Perceptron.…”

Section: Earthquake Prediction Methodsmentioning

confidence: 99%

“…i represents the RMSE of a specific algorithm in our experiments. We set the null hypothesis(H 0 ) and alternative hypothesis(H 1 ) as Eqs (13)(14)…”

mentioning

confidence: 99%

Elite GA-based Feature Selection of LSTM for Earthquake Prediction

Lan

Wen

et al. 2023

Preprint

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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.

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“…Sensitivity analysis showed that moment magnitude and source depth are the main factors affecting significant duration, while epicentral distance and shear wave velocity have little influence on significant duration. Chanda [ 14 ] developed prediction models for significant duration using neural networks, decision trees, random forests, Adaboost, and SVM based on the earthquake ground motion database in Chile, discovering that the tree-based model exhibited the best prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Significant duration prediction of seismic ground motions using machine learning algorithms

Li,

Gao

2024

PLoS ONE

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This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.

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Duration prediction of Chilean strong motion data using machine learning

Cited by 13 publications

References 12 publications

Machine Learning for sport results prediction using algorithms

Machine Learning for sport results prediction using algorithms

Elite GA-based Feature Selection of LSTM for Earthquake Prediction

Significant duration prediction of seismic ground motions using machine learning algorithms

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