RASE: A Real-Time Automatic Search Engine for Anomalous Seismic Electric Signals in Geoelectric Data

Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the respiratory system of humans. The epidemic-related data is collected regularly, which machine learning algorithms can employ to comprehend and estimate valuable information. The analysis of the gathered data through time series approaches may assist in developing more accurate forecasting models and strategies to combat the disease. This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Forecasting is conducted utilizing state-of-the-art mathematical and deep learning models for multivariate time series forecasting, including extended susceptible-exposed-infected-recovered (SEIR), long-short-term memory (LSTM), and vector autoregression (VAR). The SEIR model has been extended by integrating additional information such as hospitalization, mortality, vaccination, and quarantine incidences. Extensive experiments have been conducted to compare deep learning and mathematical models that enable us to estimate fatalities and incidences more precisely based on mortality in the eight most affected nations during the time of this research. The metrics like mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are employed to gauge the model’s effectiveness. The deep learning model LSTM outperformed all others in terms of forecasting accuracy. Additionally, the study explores the impact of vaccination on reported epidemics and deaths worldwide. Furthermore, the detrimental effects of ambient temperature and relative humidity on pathogenic virus dissemination have been analyzed.

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“… 2020 ), earthquake prediction (Xue et al. 2021 ), signal processing (Wang et al. 2023 ), pattern recognition (Wu et al.…”

Section: Literature Surveymentioning

confidence: 99%

Multivariate time series short term forecasting using cumulative data of coronavirus

Mishra¹,

Singh

Kumar

et al. 2023

Evolving Systems

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“…Researchers have employed non-seismic indicators in ANN and have discovered some supportive evidence for the efficacy of their models in predicting earthquakes. These non-seismic signals include Seismic Electric Signals (Astuti et al, 2014;Konstantaras et al, 2007;Mejia et al, 2015;Moustra et al, 2011;Xue et al, 2022Xue et al, , 2023, water level (Jiang et al, 2009;Shao et al, 2012), rainfall rate (Hajikhodaverdikhan et al, 2018;Suratgar et al, 2008), pressure (Külahcı et al, 2009), radon concentration (Külahcı et al, 2009;Mir et al, 2022), geo-magnetic and solar indices (Akhoondzadeh, 2013), oxygen ion density (Ma et al, 2010;Xu et al, 2010), helium ion density (Ma et al, 2010;Xu et al, 2010), ion temperature (Hafeez et al, 2021;Ma et al, 2010;Xiong et al, 2022;Xu et al, 2010), electron density (Hafeez et al, 2021;Ma et al, 2010;Xu et al, 2010), ground temperature (Suratgar et al, 2008), hydrochemistry (Jiang et al, 2009), methane concentration obtained by satellite data (Xiong et al, 2021) and so on. See also Mearns and Sornette (2021) and Sornette et al (2021) for a review of the use of an ensemble of non-seismic precursors in the prediction of the historical Haicheng and Tangshan earthquakes in China.…”

Section: Introductionmentioning

confidence: 99%

Seismically Informed Reference Models Enhance AI‐Based Earthquake Prediction Systems

Zhang,

Zhan,

Huang

et al. 2024

JGR Solid Earth

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Given the robust nonlinear regression capabilities of Artificial Intelligence (AI) technology, its commendable performance in numerous geophysical tasks is expected. Yet, AI technology suffers from (a) its “black box” nature and (b) the fact that some complicated artificial neural networks (ANNs) claiming superior performance do not surpass some simple geophysical models that clearly describe the underlying physical processes. Numerous reports rely on standard machine learning metrics, often using a spatially uniform Poisson (SUP) distribution as their reference. A good performance just means that the artificial neural network (ANN) outperforms this basic reference, potentially offering little novelty to the scientific community. Worse, this can lead to spurious inference. We demonstrate this by using the monthly average human‐made Nighttime Light Map and the cumulative energy of earthquakes in various space‐time units as inputs for an Long short‐term memory model. The goal is to predict earthquakes with a magnitude of M ≥ 5.0 across the entire Chinese Mainland. With the SUP reference model, the ANN concludes that human‐made Nighttime Light possesses substantial earthquake prediction capability. This is evidently flawed reasoning. We show that this stems from the poor reference model and this spurious inference disappears when using a better benchmark consisting of a spatially varying Poisson (SVP) model informed from statistical seismology. This is implemented by weighting the punishments/rewards of our ANN associated with failed/successful predictions by prior probabilities provided by the stronger SVP model. Scores obtained with the time‐space Molchan diagram demonstrate the strong performance improvement obtained by training ANN with a better reference model.

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Identifying mangroves through knowledge extracted from trained random forest models: An interpretable mangrove mapping approach (IMMA)

Zhao

Jia

Wang

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

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RASE: A Real-Time Automatic Search Engine for Anomalous Seismic Electric Signals in Geoelectric Data

Cited by 5 publications

References 58 publications

Multivariate time series short term forecasting using cumulative data of coronavirus

Multivariate time series short term forecasting using cumulative data of coronavirus

Seismically Informed Reference Models Enhance AI‐Based Earthquake Prediction Systems

Identifying mangroves through knowledge extracted from trained random forest models: An interpretable mangrove mapping approach (IMMA)

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