Integrated analysis of atmospheric and ionospheric precursors using SARIMAX, NARX, and LSTM approaches for the 2024 Mw 7.4 Taiwan earthquake

Earthquakes are among the most damaging natural hazards, highlighting the need for improved monitoring frameworks and rigorous analysis of potential precursory signals. The Mw 7.4 Taiwan earthquake provides a relevant case for evaluating methodologies to identify and interpret atmospheric and ionospheric anomalies in seismically vulnerable regions. In this study, satellite-based Remote Sensing (RS) products and Global Navigation Satellite System (GNSS) observations are integrated to examine candidate precursors, including Outgoing Longwave Radiation (OLR), Relative Humidity (RH), Air Temperature (AT), Air Pressure (AP), and Total Electron Content (TEC). Using statistical approaches, including the standard deviation (STDEV) method and the Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, together with machine-learning frameworks such as the Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Long Short-Term Memory (LSTM) networks, this study identified synchronized anomalies approximately 5–6 days prior to the event. In addition, geomagnetic perturbations were observed approximately nine days before the event, coinciding with a pronounced geomagnetic storm (Kp > 8; Dst < −120 nT; ap > 225 nT). To limit the influence of background variability and potential false alarms, a historical comparative analysis was performed using atmospheric parameters from the same region and comparable time window across the preceding five years, which further supported the robustness of the observed anomalies. By integrating statistical detection, spatial screening, and time-series forecasting models, this work contributes to a more detailed understanding of atmospheric–ionospheric signals associated with seismic activity and highlights the value of multi-parameter monitoring for seismic hazard assessment and risk-reduction planning.