Enhancing Model Transparency in Geothermal Settings: Clustering to Reduce Aleatoric Uncertainty

Aleatoric uncertainty, inherent in the variability of data itself, presents a significant challenge in predictive modeling, especially in scenarios with intrinsic randomness and noise. Traditionally viewed as irreducible, this type of uncertainty fundamentally limits the precision of predictions, as it is directly tied to the stochastic nature of the underlying data. However, this research proposes a methodology that combines clustering with subsequent predictive modeling to mitigate the effects of aleatoric uncertainty, thereby enhancing the transparency and reliability of model outputs. Our approach begins with a clustering process, where data points are grouped based on similarity in features to form homogeneous subsets. Following clustering, we employ quantile random forests on each subset rendering the modeling tailored to each cluster's specific characteristics. This strategy allows for the models to not only be more sensitive to the subtle nuances within a group but also more robust against the noise inherent in the dataset. Finally, we estimate heat flow over continental Africa. Through extensive quantitative analysis, this study demonstrates that while aleatoric uncertainty is indeed irreducible from a theoretical standpoint, practical interventions like quality data acquisition combined with clustering can effectively diminish its impact on predictive accuracy.