LSTM-Based Deep Learning Methods for Prediction of Earthquakes Using Ionospheric Data

ABRİ, Rayan; Artuner, Harun

doi:10.35378/gujs.950387

Cited by 10 publications

(2 citation statements)

References 34 publications

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“…If the output of the forget gate is 1, then the entire information is remembered and if the output is 0, then the forget gate discards the information. The output gate gives the values after applying the "tanh", which regulates the values flowing through the network and also the output to the hidden state of the next cell, as shown in Figure 1 [42,45,48].…”

Section: Long-short Term Memory Modelmentioning

confidence: 99%

Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network

et al. 2022

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The forecasting of ionospheric electron density has been of great interest to the research scientists and engineers’ community as it significantly influences satellite-based navigation, positioning, and communication applications under the influence of space weather. Hence, the present paper adopts a long short-term memory (LSTM) deep learning network model to forecast the ionospheric total electron content (TEC) by exploiting global positioning system (GPS) observables, at a low latitude Indian location in Bangalore (IISC; Geographic 13.03° N and 77.57° E), during the 24th solar cycle. The proposed model uses about eight years of GPS-TEC data (from 2009 to 2017) for training and validation, whereas the data for 2018 was used for independent testing and forecasting of TEC. Apart from the input TEC parameters, the model considers sequential data of solar and geophysical indices to realize the effects. The performance of the model is evaluated by comparing the forecasted TEC values with the observed and global empirical ionosphere model (international reference ionosphere; IRI-2016) through a set of validation metrics. The analysis of the results during the test period showed that LSTM output closely followed the observed GPS-TEC data with a relatively minimal root mean square error (RMSE) of 1.6149 and the highest correlation coefficient (CC) of 0.992, as compared to IRI-2016. Furthermore, the day-to-day performance of LSTM was validated during the year 2018, inferring that the proposed model outcomes are significantly better than IRI-2016 at the considered location. Implementation of the model at other latitudinal locations of the region is suggested for an efficient regional forecast of TEC across the Indian region. The present work complements efforts towards establishing an efficient regional forecasting system for indices of ionospheric delays and irregularities, which are responsible for degrading static, as well as dynamic, space-based navigation system performances.

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Section: Long-short Term Memory Modelmentioning

confidence: 99%

Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network

et al. 2022

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“…When it comes to real-world AI applications, the breadth of possibilities is striking. AI-driven techniques play a pivotal role in forecasting Turkey's natural gas consumption [10], utilizing LSTM-based deep learning methods for earthquake prediction through ionospheric data analysis [11], and improving the precision of daily wind energy predictions through machine learning and statistical techniques [12]. In the healthcare sector, AI comes to the forefront with a machine learning model for diagnosing Type 2 diabetes based on health behavior [13], while in the field of speech recognition, recurrent units like LSTM and GRU find applications in Turkish speech recognition techniques and broader speech processing endeavors [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

Öz,

Özarpa

2024

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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This study uses neural networks to explore the intricate longitudinal progression of decommissioned geostationary satellites. The goal is to model and predict satellites' longitudinal dynamics across time dimensions. Historical satellite longitude data undergoes thorough preprocessing to train time series neural networks in both single-input and 3-input configurations for all six decommissioned satellites, yielding comprehensive longitudinal behavior insights. Results reveal impressive outcomes: average Mean Squared Error (MSE) between predicted and measured longitudes is 1.55x10-3, with regression close to unity. This convergence implies a strong alignment between the neural network methodology employed and the intricate problem domain. These results accentuate the suitability and effectiveness of the chosen neural network approach in addressing the challenges posed by decommissioned geostationary satellite trajectory modeling. The study's implications span various fields. Insight into long-term orbital shifts aids in understanding satellite behaviors, enhancing trajectory predictions and decision-making in satellite management and space technology advancement. Additionally the research emphasizes the importance of accurate predictions about satellite behavior after decommissioning. This contributes to better mission planning, resource optimization, and more efficient strategies for dealing with space debris.

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A study of ionospheric anomaly detection before the August 14, 2021 Mw7.2 earthquake in Haiti based on sliding interquartile range method

Chen,

Meng,

Wang

et al. 2023

Acta Geod Geophys

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LSTM-Based Deep Learning Methods for Prediction of Earthquakes Using Ionospheric Data

Abstract: Highlights• The paper focuses on earthquake prediction processes using ionospheric day by day variabilities.• Due to the data capabilities, LSTM models are proposed for earthquake prediction.• Highly precise accuracy was obtained to perform earthquake prediction.

Cited by 10 publications

References 34 publications

Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network

Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

A study of ionospheric anomaly detection before the August 14, 2021 Mw7.2 earthquake in Haiti based on sliding interquartile range method

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