Dijital Tuğla – Bölüm 19: Yapay Zeka ve Depremler, Tahminden Dayanıklılığa…

15 Aug 2025 · 44 min
44:09

In this episode of the Dijital Tuğla podcast, hosts Haluk Eğinat and Cem Kafadar explore how artificial intelligence (AI) can be applied to earthquake science, preparedness, and response. They begin by emphasizing that AI’s power comes from massive, continuously collected seismic data (global seismic networks, InSAR, smartphone accelerometers, satellite radar) and that without data AI cannot function. They discuss the impossibility of exact earthquake timing prediction, but note that AI can improve probabilistic forecasts, detect subtle precursory deformations, and reduce false‑alarm rates. The conversation surveys several real‑world pilots: Caltech’s deep‑learning models on INSAR data that detect micro‑deformations, Google’s Android‑based early‑warning system that claims a one‑minute lead‑time, Japan’s JMA and MyShake applications that combine seismic data with AI, and the U.S. ShakeAlert system. They then shift to post‑event uses of AI, such as computer‑vision drones that map damage, AI‑driven structural health monitoring, and AI‑optimized logistics for rescue routes, medical supply distribution, and traffic management. Social‑media mining is highlighted as a way to filter genuine aid requests from noise. Ethical and societal issues are examined, including algorithmic bias, data privacy under Turkey’s KVKK, inclusivity of low‑resource regions, and the need for transparent, community‑driven governance. The hosts propose a four‑step roadmap: build open seismic data infrastructures, run pilot AI projects (damage mapping, micro‑tremor mapping), conduct AI‑supported resilience drills, and embed ethical frameworks (transparency, fairness, public participation). Throughout, the tone remains conversational and cautiously optimistic, acknowledging both the transformative potential of AI for earthquake resilience and its current technical and societal limits.

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