Interpretable data-driven prediction methods for atmospheric chloride deposition rate

Abstract

Precise estimation of atmospheric chloride deposition is crucial in environmental science, providing valuable insights into atmospheric pollution, metal corrosion, and the hydrologic cycle. The distribution of atmospheric chloride deposition involves multiple processes of production, transportation, and deposition, making it a problem with well-defined physical mechanisms. Although numerous parametric models have been proposed for atmospheric chloride distribution prediction, they present challenges in terms of generalizability and prediction accuracy due to simplifications of the holistic process. In contrast, non-parametric machine learning algorithms can analyze complex issues and have good prediction performance, but they are used less often in atmospheric chloride deposition modeling. Therefore, we develop a genetic algorithm optimized quantile regression forest (GA-QRF) model, incorporating environmental variables associated with the holistic deposition process to predict the distribution of atmospheric chloride deposition. QRF is an ensemble learning method known for delivering precise and interpretable predictions, coupled with effective uncertainty estimation. The proposed model results achieved an R2 value of 0.935 for median prediction and 98.2% coverage of the intervals for uncertainty quantification on a dataset collected from six countries. In addition, in order to verify the consistency of the black-box machine learning model with the domain knowledge, several interpretation methods are used to deconstruct the proposed model. Considering the spatial variability of atmospheric chloride deposition, the proposed model was further used to create deposition distribution maps.