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Spatiotemporal phenology research with interpretable models

Predicting the day of first bloom of the the common lilac, based on indirect observations/proxies

Climate change is widespread and intensifying rapidly. Temperatures continue to rise; droughts, forest wildfires and floods caused by extreme weather are becoming more frequent and severe. Climate change is undeniably impacting our planet and, as such, altering plants’ distribution and growth. The timing of plant’s life cycle events (like leafing) is clearly changing. Phenology is the science that studies these changes, their causes and interrelations.

This project aims at supporting such studies by providing efficient tools that facilitate and improve the process of discovering patterns and knowledge from phenological and environmental spatio-temporal data. These data are voluminous and characterized by complex spatial, temporal, and spatio-temporal correlations. This project proposes a regression machine learning technique that allows dealing with such complex data and providing both accurate and interpretable predictions. It will help experts to better understand phenological changes and more accurately analyze the impact of environmental and climate drivers on plants.

Participating organisations

Environment & Sustainability
Environment & Sustainability
Netherlands eScience Center
University of Twente

Output

Team

Fakhereh (Sarah) Alidoost
Fakhereh (Sarah) Alidoost
MK
Mahdi Khodadadzadeh
Lead Applicant
University of Twente
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center
Raul Zurita Milla
Raul Zurita Milla
Research team member
University of Twente
Yang Liu
Yang Liu

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