Speaker
Description
One key task in environmental science is the continuous mapping of environmental variables across space, and often across both space and time. Machine learning algorithms are frequently employed for this purpose, combining local field observations with comprehensive sets of predictor variables to produce spatial predictions. This enables the prediction of the variable of interest at locations where measurements are unavailable. However, the application of machine learning strategies for spatial mapping involves additional challenges compared to ”non-spatial” prediction tasks that often originate from spatial autocorrelation and from training data that are not independent and identically distributed.
In the past few years, we have developed several methods to support the application of machine learning to spatial data. These include prediction-domain adaptive cross-validation for performance assessment and model selection, spatial predictor variable selection, and approaches to assess the area of applicability of trained models. The objective of the CAST package is to facilitate predictive mapping with machine learning by implementing these methods and making them easily accessible for integration into modeling workflows.
Here, we present the CAST package and its core functionalities, describing both its conceptual framework and practical applications. Using a plant species richness case study, we walk through the main steps of a modelling workflow and demonstrate how CAST can be used to support more reliable spatial predictions.
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Additional Material or Paper
https://doi.org/10.1007/978-3-031-99665-8_11
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | machine learning, spatial modelling, model validation, spatial prediction, mapping |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |