Temperature Prediction at Street Scale During a Heat Wave Using Random Forest

The rising frequency of heatwaves, combined with the urban heat island effect, increases
the population’s exposure to high temperatures, significantly impacting the health of vul-
nerable groups and the overall well-being of residents. While mesoscale meteorological
models can reliably forecast temperatures across urban neighbourhoods, dense networks of
in situ measurements offer more precise data at the street scale. In this work, the Random
Forest technique was used to predict street-scale temperatures in the downtown area of
Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained us-
ing data from a low-cost sensor network, meteorological fields calculated by the mesoscale
model MEMO, and micro-environmental spatial features. The results show that, although
the MEMO temperature predictions achieve high accuracy during nighttime compared to
measurements, they exhibit inconsistent trends across sensor locations during daytime, in-
dicating that the model does not fully account for microclimatic phenomena. Additionally,
by using only the observed temperature as the target of the Random Forest model, higher
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accuracy is achieved, but spatial features are not represented in the predictions. In contrast,
the most reliable approach to incorporating spatial characteristics is to use the difference
between observed and mesoscale temperatures as the target variable.