Abstract
Decision makers (DMs) who are involved in urban planning are often required to allocate finite resources (say, money) to improve outdoor thermal comfort (OTC) levels in a region (e.g., city, canton, country). In this paper, for the first time, we address the following two questions, which are directly related to this requirement: (1) How can the statistical properties of the spatial risk profile of an urban area from an OTC perspective be quantified, no matter which OTC index the DM chooses to use? (2) Given the risk profile, how much and where should the DM allocate the finite resources to improve the OTC levels? We answer these fundamental questions by developing a new and rigorous mathematical framework as well as a new class of models for spatial risk models. Our approach is based on methods from machine learning: first, a surrogate model of the OTC index that provides both accuracy and mathematical tractability is developed via regression analysis. Next, we incorporate the imperfect climate model and derive the statistical properties of the OTC index. We present the concept of spatio-temporal aggregate risk (STAR) measures and derive their statistical properties. Finally, building on our derivations, we develop a new algorithm for spatial resource allocation, which is useful for DMs and is based on modern portfolio theory. We implemented the tool and used it to illustrate its operation on a practical case of the large-scale area of Singapore using a WRF climate model.
Highlights
An accurate assessment of the environmental risk of urban climate events is of great importance for populations, authorities, and decision makers (DMs) [1,2]
We move to developing the surrogate model for the heat index, which is a widely used outdoor thermal comfort (OTC) index
We developed a rigorous mathematical framework and a new class of statistical models for spatial risk models
Summary
An accurate assessment of the environmental risk of urban climate events is of great importance for populations, authorities, and decision makers (DMs) [1,2]. While there are a myriad of aspects that DMs should take into account, one aspect which is widely accepted as important is the perception of thermal comfort experienced by the population [6,7]. While rational indices are based on fundamentals of physics and bio-meteorology, thereby making them more favorable than their empirical counterparts, they may be more complicated and less intuitive to understand. They lack mathematical tractability and can only be evaluated through numerical calculations; see, for example, the evaluation of physiological equivalent temperature (PET) in [14]
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