Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time
Final version: https://doi.org/10.1080/13658816.2021.1965608
ABSTRACT: Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain them. Methodologically aspatial ML algorithms with an apparent high predictive power ignore characteristic non-stationary domain relationships in spatio-temporal data (e.g., dependence, heterogeneity), and can lead to incorrect interpretations and poor management decisions. This study addresses this critical methodological issue of ‘interpretability’ in ML-based modeling of structural relationships between spatio-temporal events and corresponding bio-physical drivers. Specifically, we present and evaluate a spatio-temporally interpretable random forest (iST-RF) modeling framework using the example of drivers of wildfire characteristics across the United States. Our experiments show that the spatio-temporal sampling and weighted prediction approach can improve predictive accuracy (76%) compared to the aspatial RF approach (70%), while also enhancing interpretations of the ML model’s spatio-temporal relevance for its ensemble prediction. This novel approach can help balance prediction and interpretation with fidelity in a spatial data science life cycle. However, challenges exist for predictive modeling when dataset is very small, because in such cases locally optimized sub-model’s prediction performance can be suboptimal. With that caveat, our proposed approach is an ideal choice for identifying drivers of spatio-temporal events at country or regional-scale studies.
|Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time
|CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
|August 31, 2021
|Publisher Identifier (DOI)
|September 05, 2021
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