Ugo Martinez - PhD student in Physics

Machine learning methods for predicting the urban microclimate

Supervisors: Pietro Salizzoni, Emmanuel Dellandrea, Lionel Soulhac.

Labs: LIRIS, LMFA.

Summary: My research focuses on evaluating the effectiveness of urban greening scenarios in Lyon and other cities to mitigate heat stress. I am developing a dual-track modeling approach that utilizes citizen data from Netatmo sensors to compare and integrate two distinct systems: the deterministic MATHIS model, which is based on physics-driven street-network equations, and data-driven machine learning algorithms. By leveraging these complementary methods, the project aims to refine street-level climate simulations and ultimately provide essential decision-support tools for local authorities to implement effective climate adaptation strategies.