Applying support vector machine (SVM) using GPS-TEC and Space Weather parameters to distinguish ionospheric disturbances possibly related to earthquakes

Melgarejo-Morales, Angela; Esteban Vazquez-Becerra, G.; Millan-Almaraz, J.R.; Martinez-Felix, Carlos A.; Shah, Munawar

doi:10.1016/j.asr.2023.08.028

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…The multiple analyses performed for the LAIC hypothesis development found legitimate atmospheric and ionospheric anomalies associated with earthquakes [62][63][64][65][66]. The incorporation of machine learning also improved the credibility of the precursors to some extent by removing the biases in the data [67][68][69][70][71]. On the other hand, the need to integrate more data and their possible relations has still not been addressed, as in previous reports [72][73][74][75].…”

Section: Discussionmentioning

confidence: 95%

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

Haider,

Shah,

et al. 2024

Remote Sensing

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Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon.

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Section: Discussionmentioning

confidence: 95%

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

Haider,

Shah,

et al. 2024

Remote Sensing

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“…In recent years, artificial intelligence has gradually emerged, underpinning systems such as the BP neural network, RBF neural network and SVM, which have gradually been applied to predict the hazard of water and mud inrush disasters in tunnels [7][8][9][10][11][12]. Although these methods are all used to determine the hazard of water inrush in tunnels, the performances of the models are different.…”

Section: Introductionmentioning

confidence: 99%

Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm

Zhang,

Niu,

Wan

et al. 2024

Applied Sciences

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To prevent large-scale water inrush accidents during the excavation process of a water-rich tunnel, a method, based on a random forest (RF) algorithm, for predicting the hazard level of water inrush is proposed. By analyzing hydrogeological conditions, six factors were selected as evaluating indicators, including stratigraphic lithology, inadequate geology, rock dip angle, negative terrain area ratio, surrounding rock grade, and hydrodynamic zonation. Through the statistical analysis of 232 accident sections, a dataset of water inrush accidents in water-rich tunnels was established. We preprocessed the dataset by detecting and replacing outliers, supplementing missing values, and standardizing the data. Using the RF model in machine learning, an intelligent prediction model for the hazard of water inrush in water-rich tunnels was established through the application of datasets and parameter optimization processing. At the same time, a support vector machine (SVM) model was selected for comparison and verification, and the prediction accuracy of the RF model reached 98%, which is higher than the 87% of the SVM. Finally, the model was validated by taking the water inrush accident in the Yuanliangshan tunnel as an example, and the predicted results have a high degree of consistency with the actual hazard level. This indicates that the RF model has good performance when predicting water inrush in water-rich tunnels and that it can provide a new means by which to predict the hazard of water inrush in water-rich tunnels.

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A comprehensive study on the synchronized outgoing longwave radiation and relative humidity anomalies related to global Mw ≥ 6.5 earthquakes

Shah,

Draz,

Saleem

2023

Nat Hazards

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Applying support vector machine (SVM) using GPS-TEC and Space Weather parameters to distinguish ionospheric disturbances possibly related to earthquakes

Cited by 11 publications

References 36 publications

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm

A comprehensive study on the synchronized outgoing longwave radiation and relative humidity anomalies related to global Mw ≥ 6.5 earthquakes

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