AI AND GIS FOR EARTHQUAKE EARLY WARNING AND EVACUATION PLANNING IN ALMATY

Abstract

Almaty, Kazakhstan’s largest city, sits at the foothills of the Tien Shan mountains and is exposed to high seismic hazards and related cascading events. Historical earthquakes such as the 1911 Kebin event (Mw 8.0) that killed 452 people and destroyed more than 770 buildings illustrate the city’s vulnerability, while more recent mudflows have prompted mass evacuations. Modern developments, including plans for a nationwide mass alert system using cell broadcast technology, raise the prospect of integrating artificial intelligence (AI) and geographic information systems (GIS) for real time earthquake early warning and evacuation planning. This article develops a comprehensive AI + GIS framework that combines deep learning–based P-wave detection (CSESnet), machine learning models for emergency shelter site selection, dynamic GIS-based evacuation route optimisation, and rapid public alert dissemination through cell broadcast technology. Results show that AI models can significantly improve detection accuracy and decision-making speed, while GIS enables adaptive routing under multi-hazard conditions. The originality of the work lies in its modular architecture tailored to Almaty’s risk landscape, which emphasises interpretability, public engagement, and community preparedness. The framework provides a transferable model for cities in other seismically active regions seeking to integrate AI into disaster risk reduction