Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
arXiv:2607.07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM$_{2.5}$ concentrations that pose severe public health risks, yet forecasting rare, hazardous-level spikes remains a fundamental challenge. Time series foundation models (TSFMs), pretrained models offering zero-shot inference and efficient adaptation, perform strongly on general benchmarks, but their behavior under extreme out-of-distribution conditions is poorly understood. We present the first systematic benchmark comparing six TSFM configurations (zero-shot TimesFM, Chronos-2, Moirai-2, and Time-MoE, plu
The proliferation of foundation models creates an urgent need to evaluate their reliability and generalizability, particularly in critical applications like environmental forecasting where real-world risks are high.
This research provides crucial insights into the limitations and applicability of AI foundation models for extreme, out-of-distribution events, directly impacting their trustworthy deployment in critical infrastructure and public safety.
The understanding of foundation model robustness for extreme environmental prediction is enhanced, potentially leading to more targeted development and more cautious real-world deployment strategies in high-stakes scenarios.
- · AI Safety Researchers
- · Environmental Monitoring Agencies
- · Public Health Sector
- · Cloud Computing Providers
- · Developers neglecting domain-specific testing
- · Purely generalist AI solution providers
Improved accuracy in forecasting extreme environmental events like wildfires, enabling earlier warnings and more effective public health interventions.
Increased demand for specialized foundation models or fine-tuning techniques capable of handling rare, high-impact events and out-of-distribution data.
Potential for new regulatory frameworks and certification processes for AI models used in critical forecasting applications, emphasizing robust performance under extreme conditions.
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Read at arXiv cs.LG