Graduation of Paul Koot

Rapid assessment tool to quantify the spatial influence of surfaces on heat stress

• Professor of graduation: Dr. ir. O. Morales Nápoles (TU Delft)

• Supervisors of graduation: Dr. ir. J. A. A. Antolínez (TU Delft), Ir. G. A. Torres Alves (TU Delft), Dr. ir. E. Aparicio Medrano (Nelen & Schuurmans), I. Lokhorst MSc (Nelen & Schuurmans)

Climate change causes cities to deal with increased temperatures and more frequent weather extremes. Heat waves will occur more often, becoming a more prevalent issue in especially urban areas. Quantification of heat stress is a first step to mitigation measures. To this end, a standardised method to assess the spatial influence of surfaces on the Physical Equivalent Temperature (PET) was developed. This study aims to reshape this model into a statistical dependence model which is more flexible regarding missing data. To this end, we used a Non-Parametric Bayesian Network (NBPN). We created a model driven by both data and expert knowledge, that is capable of dealing with input data layers with a grid resolution up to 20 m. Results show that training the model on only 20 sample points gave the same performance as using 2,000. The model predicts the PET with a Mean Absolute Error (MAE) of 1 to 2 °C, dealing adequately with missing data layers. With this limited amount of necessary input, the NPBN in our study helps in standardising the assessment of heat stress outside the borders of the Netherlands. Also, our model offers a framework to make a first assessment regarding the effect of NBSs on heat stress.