Multi-objective optimization of urban environmental system design using machine learning
Multi-objective optimization of urban environmental system design using machine learning
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DOI：
10.1016/j.compenvurbsys.2022.101796
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Peiyuan Li;Tianfang Xu;Shiqi Wei;Zhihong Wang
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文献类型：
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作者：
Peiyuan Li;Tianfang Xu;Shiqi Wei;Zhihong Wang
城市热和碳排放缓解战略的有效性在很大程度上取决于当地的城市特点。城市陆面模型的不断发展和改进使得能够相当准确地评估环境对城市发展战略的影响，而由于城市系统动力学的日益复杂，基于物理的模拟仍然是计算昂贵和耗时的结果。因此，迫切需要为城市规划者开发快速、高效和经济的操作工具包，以促进城市缓解战略的设计、实施和评估，同时保持物理模型的准确性和健壮性。在本研究中，我们采用了一种机器学习算法，即。高斯过程回归，以模拟建筑环境中热量和生物碳交换的物理过程。ML代理在一种最先进的单层城市树冠模型生成的模拟结果上进行了训练和验证，该模型涵盖了广泛的城市特征，在捕获热量和碳动态方面表现出高精度。然后利用经过验证的代理模型，使用遗传算法进行多目标优化，以优化城市设计场景，以获得理想的城市缓解效果。虽然城市绿化的使用被发现在缓解城市热量和碳排放方面都有效，但在改善各种城市环境指标之间存在明显的权衡。
The efficacy of urban mitigation strategies for heat and carbon emissions relies heavily on local urban characteristics. The continuous development and improvement of urban land surface models enable rather accurate assessment of the environmental impact on urban development strategies, whereas physically-based simulations remain computationally costly and time consuming, as a consequence of the increasing complexity of urban system dynamics. Hence it is imperative to develop fast, efficient, and economic operational toolkits for urban planners to foster the design, implementation, and evaluation of urban mitigation strategies, while retaining the accuracy and robustness of physical models. In this study, we adopt a machine learning (ML) algorithm, viz. Gaussian Process Regression, to emulate the physics of heat and biogenic carbon exchange in the built environment. The ML surrogate is trained and validated on the simulation results generated by a state-of-the-art single-layer urban canopy model over a wide range of urban characteristics, showing high accuracy in capturing heat and carbon dynamics. Using the validated surrogate model, we then conduct multi-objective optimization using the genetic algorithm to optimize urban design scenarios for desirable urban mitigation effects. While the use of urban greenery is found effective in mitigating both urban heat and carbon emissions, there is manifest trade-offs among ameliorating diverse urban environmental indicators.