Our seismic research introduces a transformative predictive model for earthquake safety, driven by data analytics and cutting-edge machine learning technology.

The influence of solar activity on seismic activity is a subject of debate. Previous studies have shown that there is sometimes a correlation and sometimes a contradiction between solar activity maxima and large earthquakes. Long-term memory neural network is used to study the relationship between solar activity and seismic activity. This study emphasizes retrospective classification rather than direct prediction, refining the LSTM architecture to maximize classification accuracy and processing data from the Solar and Heliospheric Observatory and the U.S. Geological Survey earthquake catalogs. A declustering technique is used to select large seismic events and weighted learning corrects for class imbalances. The LSTM model accurately classified earthquakes (84.47%) and proton density variations. The results support the theory that solar activity, in particular proton density, can anticipate earthquake events.

To explore the application of neural networks for estimating geomagnetic field disturbances, this study pays particular attention to K-index classification. The primary goal is to develop a robust and efficient method for classifying different levels of geomagnetic activity using neural networks. Our work encompasses data preprocessing, model architecture optimization, and a thorough evaluation of classification performance. A new neural-network approach is proposed to address the specific complexities of geomagnetic data, and its merits are compared with those of conventional techniques. Notably, Long Short-Term Memory (LSTM) models significantly outperformed traditional methods, achieving up to 98% classification accuracy. These findings demonstrate that neural networks can be effectively applied in geomagnetic studies, supporting AI-based forecasting and enabling further integration into space weather research

Earthquake forecasting using traditional methods remains a complex task due to the inherent nonlinearity and stochastic nature of seismic activity. Therefore, this study examines the application of deep learning methods, particularly a hybrid model combining Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), to create a robust prediction model that outperforms traditional methods. The study comprises six primary phases: data acquisition, pre-processing, exploratory analysis, seismicity characterization, model implementation, and effectiveness assessment. The dataset contained 17,565 earthquake events (M ≥ 3.0) in Central Asia, collected from 1973–2024 via the USGS API, enriched with geological features such as distance to active faults (from GEM datasets) and eight key seismicity indicators. Compared with baseline architectures (standard LSTM and fully connected neural network), the proposed CNN-LSTM hybrid achieved superior performance, particularly in recall (0.80 compared with 0.67) and F1-score (0.77 compared with 0.71), demonstrating its ability to capture both spatial and temporal dependencies. The results indicate that using a hybrid CNN-LSTM model leads to a significant improvement in forecasting strong earthquakes. This is validated by ROC-AUC of 0.78 and F1-score of 0.77, supporting a good balance between model completeness and accuracy. These improvements confirm that the regional adaptation of CNN-LSTM, combined with seismic indicators and clustering-based subregionalization, provides a novel methodological contribution to earthquake forecasting in Central Asia. The model reliably distinguishes periods of elevated seismic risk from quiet intervals, supporting practical applications in regional forecasting and seismic risk visualization.

Devised intricate mathematical models for poroelastic composite media, validated through rigorous numerical experiments, ensuring exceptional stability and groundbreaking insights.

Justified the possibility of a system of preliminary road diagnostics, leveraging only openly accessible remote sensing and meteorological information.