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. Mean radiant temperature (Tmrt) is a frequently usedmeasure of outdoor radiant heat conditions. Excessive Tmrt, linkedespecially to clear and warm days, has a negative effect on humanwellbeing. The highest Tmrt on such days is found in sunlit areas,whereas shaded areas have significantly lower values. One way of alleviatinghigh Tmrt is by planting trees to provide shade in exposed areas.Achieving the most efficient mitigation of excessive Tmrt by tree shadewith multiple trees requires optimized positioning of the trees, which is acomputationally extensive procedure. By utilizing metaheuristics, the numberof calculations can be reduced. Here, we present TreePlanter v1.0, whichapplies a metaheuristic hill-climbing algorithm on input raster data ofTmrt and shadow patterns to position trees in complex urban areas. Thehill-climbing algorithm enables dynamic exploration of the input data toposition trees, compared with very computationally demanding brute-forcecalculations. The hill-climbing algorithm has been evaluated with a staticgreedy algorithm that positions trees one at a time based on ranking and isexpected to always find relevant locations for trees. The results show thatthe hill-climbing algorithm, in relatively low model runtime, can findpositions for several trees simultaneously, which lowers Tmrtsubstantially. TreePlanter, with its two algorithms, can assist inoptimization of tree planting in urban areas to decrease thermal discomfort.

Locating trees to mitigate outdoor radiant load of humans in urban areas using a metaheuristic hill-climbing algorithm – introducing TreePlanter v1.0