Hassan Algamdi

تخصص الذكاء الاصطناعي · دفعة 2025

Evaluating Statistical and Machine Learning Methods for Future Heat Stress Downscaling in Saudi Arabia

The built environment is sensitive to global warming and climate change, which are leading to increased cooling loads, dangerous heat waves, damaging flooding in cities, permafrost degradation, and other impacts. To model, quantify, and predict these impacts for engineering analysis requires “downscaling”, which maps available climate information (e.g. weather station data, model output) to the requirements of engineering (site specific information, design requirements), while accounting for incompatible sampling, errors in observations, and uncertainty. Downscaling is a workflow of data processing and model calibration against observations that is increasingly informed by machine learning and modern data science. Over the last few years we have developed the UofT Climate Downscaling Workflow (UTCDW), a set of guides, software, and visuals to bring downscaling into engineering research and design, and piloted these tools during a Climate Impacts Hackathon in March, 2024. The SUDS scholars will improve the usability of and extend the data-science approaches in the UTCDW, with an emphasis on how to best use connections between climate fields like temperature and precipitation in our workflow, in particular to implement multvariate downscaling methods for climate extremes. The project will demonstrate how the UTCDW can effectively translate climate science knowledge and data into actionable information.

المشرف: Paul Kushner

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