Although the ultimate scientific objective is to derive interpretation in space-time, experiments show that iST-RF can improve predictive accuracy (76%) ...
Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events ...
Aug 31, 2021 · This novel approach can help balance prediction and interpretation with fidelity in a spatial data science life cycle. However, challenges exist ...
Mar 25, 2022 · This novel approach can help balance prediction and interpretation with fidelity in a spatial data science life cycle. However, challenges exist ...
2014. We present a method for developing spatially explicit probability maps for the presence of wildfire residuals within a burned landscape.
Home · Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time.
Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time pp. 692-719(28) Authors: Masrur; Yu; Mitra; Peuquet ...
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Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time. A Masrur, M Yu, P Mitra, D Peuquet, A Taylor.
Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time. Int. J. Geogr. Inf. Sci. 36(4): 692-719 (2022); 2020.
This framework works in both geographic regression and classification, offering a novel approach to understanding complex spatial phenomena.