Uncertainty Analysis, Validation, and Benchmarking of AI Models in Earth Sciences

Artificial intelligence (AI) and machine learning (ML) models have revolutionized Earth sciences, enabling unprecedented capabilities in climate prediction, remote sensing, and environmental monitoring. However, the reliability of AI-driven Earth science applications critically depends on rigorous uncertainty quantification, validation methodologies, and standardized benchmarking frameworks. This chapter comprehensively examines the state-of-the-art approaches for assessing AI model reliability in Earth sciences, covering probabilistic uncertainty estimation techniques, spatial and temporal validation strategies, and emerging benchmark datasets. We analyze deep learning uncertainty methods including Bayesian neural networks, ensemble approaches, and conformal prediction, while addressing unique challenges posed by geospatial data such as spatial autocorrelation and distribution shifts.

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