Uncertainty-aware surrogate modeling for urban air pollutant dispersion prediction

IF 7.1 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Building and Environment Pub Date : 2024-11-17 DOI:10.1016/j.buildenv.2024.112287
Eliott Lumet , Mélanie C. Rochoux , Thomas Jaravel , Simon Lacroix
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Abstract

This study evaluates a surrogate modeling approach that provides rapid ensemble predictions of air pollutant dispersion in urban environments for varying meteorological forcing, while estimating irreducible and modeling uncertainties. The POD–GPR approach combining Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR) is applied to emulate the response surface of a Large-Eddy Simulation (LES) model of the Mock Urban Setting Test (MUST) field-scale experiment. We design and validate new methods for (i) selecting the POD-latent space dimension to avoid overfitting noisy structures due to atmospheric internal variability, and (ii) estimating the uncertainty in POD–GPR predictions. To train and validate the POD–GPR surrogate in an offline phase, we build a large dataset of 200 LES 3-D time-averaged concentration fields, which are subject to substantial spatial variability from near-source to background concentration and have a very large dimension of several million grid cells. The results show that POD–GPR reaches the best achievable accuracy levels, except for the highest concentration near the source, while predicting full fields at a computational cost five orders of magnitude lower than an LES. The results also show that the proposed mode selection criterion avoids perturbing the surrogate response surface, and that the uncertainty estimate explains a large part of the surrogate error and is spatially consistent with the observed internal variability. Finally, POD–GPR can be robustly trained with much smaller datasets, paving the way for application to realistic urban configurations.
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用于城市空气污染物扩散预测的不确定性感知代用模型
本研究评估了一种代用建模方法,该方法可在估算不可还原性和建模不确定性的同时,针对不同气象强迫对城市环境中的空气污染物扩散进行快速集合预测。结合适当正交分解(POD)和高斯过程回归(GPR)的 POD-GPR 方法被用于模拟模拟城市环境测试(MUST)现场规模实验的大尺度模拟(LES)模型的响应面。我们设计并验证了以下新方法:(i) 选择 POD 潜伏空间维度,以避免因大气内部变异而过度拟合噪声结构;(ii) 估算 POD-GPR 预测的不确定性。为了在离线阶段训练和验证 POD-GPR 代理,我们建立了一个包含 200 个 LES 三维时间平均浓度场的大型数据集。结果表明,POD-GPR 达到了可实现的最佳精度水平,除了源附近的最高浓度,同时预测全场的计算成本比 LES 低五个数量级。结果还表明,所提出的模式选择标准避免了对代理响应面的扰动,不确定性估计值解释了大部分代理误差,并且在空间上与观测到的内部变异性相一致。最后,POD-GPR 可以使用更小的数据集进行稳健训练,为应用于现实城市配置铺平了道路。
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来源期刊
Building and Environment
Building and Environment 工程技术-工程:环境
CiteScore
12.50
自引率
23.00%
发文量
1130
审稿时长
27 days
期刊介绍: Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.
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