An Assessment of Tool Interoperability and its Effect on Technological Uptake for Urban Microclimate Prediction with Deep Learning Models

The benefits of deep learning (DL) models often overshadow the high costs associated with training them. Especially when the intention of the resultant model is a more climate resilient built environment, overlooking these costs are borderline hypocritical. However, the DL models that model natural phenomena— conventionally simulated through predictable mathematical modelling—don't succumb to the costly pitfalls of retraining when a model's predictions diverge from reality over time. Thus, the focus of this research will be on the application of DL models in urban microclimate simulations based on computational fluid dynamics. When applied, predicting wind factors through DL, rather than arduously simulating, can offer orders of magnitude of improved computational speed and costs. However, despite the plethora of research conducted on the training of such models, there is comparatively little work done on deploying them. This research posits: to truly use DL for climate resilience, it is not enough to simply train models, but also to deploy them in an environment conducive of rapid uptake with minimal barrier to entry. Thus, this research develops a Grasshopper plugin that offers planners and architects the benefits gained from DL. The outcomes of this research will be a tangible tool that practitioners can immediately use, toward making effectual change.