Abstract
Abstract. Mean radiant temperature (Tmrt) is a frequently used
measure of outdoor radiant heat conditions. Excessive Tmrt, linked
especially to clear and warm days, has a negative effect on human
wellbeing. The highest Tmrt on such days is found in sunlit areas,
whereas shaded areas have significantly lower values. One way of alleviating
high Tmrt is by planting trees to provide shade in exposed areas.
Achieving the most efficient mitigation of excessive Tmrt by tree shade
with multiple trees requires optimized positioning of the trees, which is a
computationally extensive procedure. By utilizing metaheuristics, the number
of calculations can be reduced. Here, we present TreePlanter v1.0, which
applies a metaheuristic hill-climbing algorithm on input raster data of
Tmrt and shadow patterns to position trees in complex urban areas. The
hill-climbing algorithm enables dynamic exploration of the input data to
position trees, compared with very computationally demanding brute-force
calculations. The hill-climbing algorithm has been evaluated with a static
greedy algorithm that positions trees one at a time based on ranking and is
expected to always find relevant locations for trees. The results show that
the hill-climbing algorithm, in relatively low model runtime, can find
positions for several trees simultaneously, which lowers Tmrt
substantially. TreePlanter, with its two algorithms, can assist in
optimization of tree planting in urban areas to decrease thermal discomfort.
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