Data-Driven Climate Risk Forecasting Using Hybrid Machine Learning Models

Abstract

This research proposes a new framework of analysis in predicting high-impact deviations in an environmental system in changing observational circumstances. The new approach, which is called Distribution-adaptive Uncertainty Synthesis (DAUS) is made to work without explicit physical assumptions or time-order-dependence, addressing instead latent structure regularities in the heterogeneous observations. DAUS combines regime invariant encoding and uncertainty resilient optimization to align with sub-surface distributional instability which is a precursor to extreme behavior of a system. The structure utilizes the dual-channel latent representations in maintaining variability and structural consistency and adaptive uncertainty synthesis mechanism dynamically increases signals linked to high deviation potential. A self-recalibration thresholding approach also allows the end-on continuous recalibration of non-stationary input distributions. In comparison to traditional predictive structures, DAUS is based on anticipatory sensitivity instead of having point estimation accuracy, enabling it to be practical in case of sudden regime changes and partial information. Experimental assessment on various benchmark data proves that the suggested strategy always yields superior results compared to current strategies in detecting high-deviation cases, especially those in the cases of distributional volatility and the presence of noise. These findings suggest that DAUS is a good and generalizable route to further study of the environmental system and has significant potentials of being integrated into decision-support pipelines where the uncertainty awareness and adaptive responsiveness is essential. The suggested technique attains an overall accuracy of roughly 91.6%, indicating its robust and equitable performance across detection reliability metrics.