Several efforts have been made to understand the complex physical processes involved in a
seismic process, but the findings are vague considering prediction capabilities …

Abstract We propose a Multi-Network-based Hybrid Long Short Term Memory (N-LSTM)
model for ionospheric anomaly detection to forecast highly irregular data of the ionospheric …

Since ionospheric variability changes dramatically before the major earthquakes (EQ), the
detection of ionospheric anomalies for EQ forecasting has been a hot topic for modern-day …

A short‐term (30 days before an earthquake) prediction of an earthquake is a big challenge
in seismology. As a first step, we apply deep learning to the ionospheric total electron …

It has been observed in many studies that ionosphere create a significant perturbation
before major earthquakes. Therefore, forecasting of earthquakes on the basis of the …

Global Navigation Satellite System (GNSS)-and Remote Sensing (RS)-based Earth
observations have a significant approach on the monitoring of natural disasters. Since the …

In this article, a new method for seismic ionospheric Global Navigation Satellite System
(GNSS) total electron content (TEC) based anomaly detection using a deep learning …

Earthquakes occur all around the world, causing varying degrees of damage and
destruction. Earthquakes are by their very nature a sudden phenomenon and predicting …

Earth observations from remotely sensed data have a substantial impact on natural hazard
surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake …

There is still no consensus on seismo-ionospheric anomalies and coupling mechanism. One
of important reasons is that multi-excitation factors of ionospheric disturbances interfere with …