Statistical Machine Learning with Functional Data for Assessment of Landscape Vulnerability to Climate Change and Land Cover Development

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Collaborative Research Team Project: 2021-2024

This project aims to bring together hydrology and statistical scientists in order to develop new generalizable statistical learning tools using multivariate functional data to (1) identify causes and consequences of environmental disturbances, (2) identify individual and interactive controls on landscape vulnerability to multi-dimensional environmental disturbances and (3) reflect the bi-directional feedback between environmental disturbances and the hydrologic function of earth systems, across distinct geographies and environmental settings in Canada.  These scientific aims will require corresponding advances in statistical modelling and analysis of multivariate functional data.

Team Leaders:

Ali Ameli, Department of Earth Ocean and Atmospheric Sciences, University of British Columbia
William Welch, Department of Statistics, University of British Columbia
Jiguo Cao, Department of Statistics and Actuarial Science, Simon Fraser University.

Collaborators:

Pierre Duchesne, Université de Montréal
Richard Arsenault, Université du Quebec à Montréal; British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development (Prince George).

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