Collaborative Research Team Project #17
Statistical Machine Learning with Functional Data for Assessment of Landscape Vulnerability to Climate Change and Land Cover Development
This project explores the assessment of landscape vulnerability to climate change and land cover development.
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.
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.
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).
Research Category: Ecology & Environment
Why Study Landscape Vulnerability?
Through climate change and land cover development, our landscapes are becoming more vulnerable. Hydrologists study the earth’s water and its movement related to land, including controls on landscape vulnerability and feedback systems from environmental disturbances.
This project aims to bring together hydrology and statistical sciences in order to develop new statistical tools and models.
Areas of Exploration
Includes causes and consequences of environmental disturbances to landscape ecology.
Includes individual and interactive controls to multi-dimensional environmental disturbances.
Includes bi-directional feedback between environmental disturbances and the hydrologic function of earth systems.
Solving Global Challenges
Research Team’s Goal
To develop new statistical learning tools for assessing landscape vulnerability to environmental disturbances.
People Behind the Project
Ali Ameli | University of British Columbia
William Welch | University of British Columbia
Jiguo Cao | Simon Fraser University
Pierre Duchesne | University of Montreal
Richard Arsenault | University of Quebec in Montreal
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Statistical Machine Learning with Functional Data for Assessment of Landscape Vulnerability to Climate Change and Land Cover Development is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.
CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.