Integrated Geotechnical Modelling and Real-time Analysis for Predicting Earthquake-Induced Landslides and Rockfalls in the East African Fracture Zone

Abstract

Background: The East African Fracture Zone (EAFZ) stands as a testament to the dynamic forces of Earth, marked by heightened seismic activity that frequently triggers geotechnical disasters such as landslides and rockfalls. Traditionally, the study of earthquake-induced geological risks has been reactive, with a focus on post-incident analysis. While significant advances have been made in spatial analysis and risk mapping, the capabilities for real-time prediction and proactive mitigation are still limited. Objectives: Current study presents an approach to predicting earthquake-induced landslides and rockfalls in the EAFZ. The aim is to change the perception of the problem by viewing disasters as manageable risks and informing decision-making in urban planning and disaster mitigation strategies. Methods: A combination of geotechnical engineering, remote sensing, artificial intelligence and machine learning, and socio-economic analysis were used to develop a holistic software framework that solves the complex problem of earthquake prediction without any problems. Results: A software model has been developed that includes a dynamic learning component that refines its predictions with new data, allowing for a deeper understanding of geological subtleties and socio-economic impacts. Considerable attention is paid to the tangible consequences of landslides and rockfalls, including human, property and economic losses. Despite the inevitable challenges of data accuracy and natural unpredictability, the proposed approach opens up new possibilities for proactive disaster management. The results demonstrate a transformational step in data-driven geotechnics and highlight the global applicability of the methods proposed in this work. Conclusion: In this investigation, was taken a pioneering stride in the realm of geotechnical hazard analysis and prediction, focusing on the complex terrains of the East African Fracture Zone (EAFZ). The results provided critical insights into the dynamics of geotechnical hazards in the EAFZ, laying the foundation for future innovations and enhanced safety measures in vulnerable communities.

Keywords

• earthquake-induced landslides
• rockfall prediction
• geotechnical engineering
• machine learning
• remote sensing
• socio-economic impact analysis