Novel approaches to model assessment and interpretation in geospatial machine learning: addressing spatial dependence and high dimensionality
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As the interpretability and explainability of artificial intelligence decisions has been gaining attention, novel approaches are needed to develop diagnostic tools that account for the unique challenges of environmental data, especially the spatial dependence of measurements and the high dimensionality of feature spaces. These are addressed by novel methods presented in this contribution with examples such as the regionalization of pollutants in the environment, and remotely-sensed mapping of mountain permafrost features as an essential climate variable and potential hazard.
Building upon the geostatistical tradition of distance-based measures, spatial prediction error profiles (SPEPs) and spatial variable importance proles (SVIPs) are introduced as novel model-agnostic assessment and interpretation tools that explore the behavior of models at different prediction horizons.
In the case study, SPEPs and SVIPs successfully highlight differences and surprising similarities among geostatistical and machine-learning algorithm. Moreover, to address the challenges of interpreting the joint effects of strongly correlated or high-dimensional features, often found in remote sensing, a model-agnostic approach is developed that distills aggregated relationships from complex models into a lower-dimensional interpretation space.
The novel diagnostic tools enrich the toolkit of geospatial data science, and may improve machine-learning model interpretation, selection, and design in a variety of geospatial application domains.
This live event includes a 15-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.