AI-Based Early Warning Systems for Climate-Driven Infrastructure Risk Assessment
Keywords:
artificial intelligence, early warning systems, climate risk, infrastructure, resilience, decision support, governanceAbstract
Climate change is intensifying the frequency and severity of extreme weather events, threatening the reliability and safety of critical infrastructure systems worldwide. Traditional risk assessment methods, which rely on historical data and stationary climate assumptions, are increasingly inadequate in a non-stationary environment. Artificial intelligence offers transformative potential for early warning systems that can anticipate climate-driven infrastructure failures and support proactive adaptation. This paper provides a systems-level examination of AI-based early warning architectures, emphasizing the interplay between data acquisition, predictive modeling, decision support, and governance. We analyze structural trade-offs inherent in the design of such systems, including the balance between model complexity and interpretability, the tension between local calibration and generalizability, and the challenges of integrating heterogeneous data streams across temporal and spatial scales. Deployment considerations such as computational sustainability, robustness to dataset shift, and fairness in risk allocation are discussed in depth. We further explore the policy and institutional implications of embedding AI into infrastructure risk governance, with attention to accountability, transparency, and the potential for maladaptation. Through case illustrations from coastal flood management, wildfire risk, and transportation network integrity, we highlight both the promises and pitfalls of AI-driven early warning. The paper concludes with a forward-looking research agenda that calls for interdisciplinary collaboration, open benchmarks, and participatory design to ensure that these systems are equitable, resilient, and aligned with societal values.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
