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

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Lead by Ali Ameli, Assistant Professor in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia.

Climate change and land cover development (for agriculture and forestry) are gradually triggering transitions in the function of earth systems, altering the magnitude and pathways of water fluxes, rate of vegetation growth, and ultimately altering landscape vulnerability to more intense, punctuated multidimensional environmental disturbances such as droughts, floods, and wildfires. With the availability of high volumes and wide variety of observational data over time and space, merging modern (eco)hydrologic process understanding with statistical methodologies that can harness the power of big data will advance assessment and prediction of landscape vulnerability to environmental disturbances.

This project aims to bring together hydrology and statistical scientists in order to develop new generalizable statistical learning tools using multivariate functional data to:

  • Identify causes and consequences of environmental disturbances
  • Identify individual and interactive controls on landscape vulnerability to multi-dimensional environmental disturbances
  • 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. This project will empower the (eco)hydrology communities to transition into a phase of data-driven functional understanding and cross-site research by creating predictive maps of landscape hydrologic processes as well as decision making tools for landscape vulnerability to environmental disturbances across Canada, taking account of climate and land cover variability.

Ali’s CRT includes two early career researchers, one mid-career researcher and two established researchers at four academic institutions in Vancouver and Montreal. 

Call for Letters of Intent

Researchers can now apply to the next round of CRTs. LOIs are due May 7, 2021. We encourage researchers from any field to apply, no matter what stage you are at in your career. (Collaborating with a statistics researcher is key)

Learn how to submit your Letter of Intent

Team Leader

Lead by Ali Ameli, Assistant Professor in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia.

Team Members

William Welch, Professor, Department of Statistics, University of British Columbia

Jiguo Cao, Canada Research Chair in Data Science, Associate Professor, Department of Statistics and Actuarial Science, Simon Fraser University 

Pierre Duchesne, Professor, Department of Mathematics and Statistics, Université du Montréal

Richard Arsenault, Associate professor, Department of Civil Engineering, École de Technologie Supérieure

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