Using Machine Learning in Decision-making to Augment Beauty, Resilience, and Sustainability Outcomes in Urban Planning

拟议的研究旨在回答如何将机器学习用于决策的问题，以增强英国城市规划，设计和工程中的美观，弹性和可持续性成果。到2050年，预计将有67亿或68%的世界人口居住在城市（联合国，2019年），这意味着发展中国家和发达国家都面临着巨大的挑战，必须在今天做出关键和战略决策。为了确保城市化现象和其他持续的全球气候变化大趋势不会导致这些城市的环境灾难或生活质量下降，全球各国政府和城市领导人都在努力应对“复杂环境中的复杂情况”（班尼特和班尼特，2008年）。通过第四次工业革命放大的数字连接产生的大量数据以及数据分析能力的同步增长，为决策和政策制定提供了良好的支持系统，然而，适用于城市，环境或可持续发展领域的许多政策或战略以及设计和工程实例仍然是专制决策的遗产，造成效率低下，从标准化的选项和清单中去政治化的解决方案（Jordan和Turnpenny，2015）。城市继续以潜在的不可逆转的方式发展和变化，因此，承认城市建筑环境的挑战非常复杂，在气候变化，技术进步，恐怖威胁，网络攻击，生物战等背景下理解这种复杂性，理解问题（这本身可能是一个反复的过程），为此寻求解决方案，了解人类的局限性是重要的第一步，并将有助于确定在何处以及何种类型的决策需要增强城市的表现，以及技术可以在何处以及如何提供帮助。文学在这些领域是有限的。为了取得成功，麦肯锡的广泛建议是，城市领导者采取战略方法，规划变革，整合环境思维，并将城市的价值主张建立在所有人的机会之上。这些建议要求，今天领导城市规划、设计和工程的利益相关者的决策不仅是创新的、协作的和创造性的，而且是基于在一个层面上从过去学习，在另一个层面上看到和塑造未来的能力。机器学习-人工智能的一个子集-具有良好的记录分类趋势和模式的能力以及处理多维，多变量大数据的能力，可以很好地：1。帮助提高对这种情况的复杂性的理解2.转变新城镇、城市和大规模混合用途城市开发（LMUD）的构思、开发和维护方式;增强新城镇、城市和LMUD的绩效成果。

The proposed research seeks to answer the question of how machine learning may be used in decision-making to augment beauty, resilience, and sustainability outcomes in urban planning, design, and engineering in the UK. BackgroundBy 2050, 6.7 billion or 68% of the world's population is expected to live in cities (UN,2019), which means that both developing and developed worlds are faced with enormous challenges and critical and strategic decisions that must be made today. To ensure that this phenomenon of urbanisation and the other enduring global megatrend of climate change do not result in environmental catastrophes or a poor quality of life in these cities, governments and city leaders, globally, are grappling with decision-making for "complex situations in complex environments" (Bennett and Bennett,2008). Vast amounts of data generated through the amplified digital connectivity of the Fourth Industrial Revolution and concurrent growth in data analytics capabilities have permitted good support systems for decision and policy-making, yet, many instances of policy or strategy as well as design and engineering as applicable to the urban, environmental or sustainability domains remain the legacy of authoritarian decisions, creating ineffective, depoliticised solutions from standardized options and checklists (Jordan and Turnpenny,2015). Cities continue to evolve and change in a potentially irreversible manner, so, acknowledging that the challenges of the urban built environment are extraordinarily complex, understanding this complexity in the contexts of climate change, technological advancements, terror threats, cyberattacks, biological warfare, etc, understanding the problem(s) (which in itself may be an iterative process) for which solutions are sought, and understanding the human limitations are important first steps, and will help with identifying where and what types of decisions are required to augment the performance of cities, and where and how technology could help. Literature is limited in these areas. To be successful, McKinsey's broad recommendations are that city leaders adopt a strategic approach, plan for change, integrate environmental thinking, and base the value proposition of their cities on opportunities for all. These recommendations demand that decision-making by stakeholders leading urban planning, design, and engineering (UPDE) today is not only innovative, collaborative, and creative but also based on an ability to learn from the past at one level and see and shape the future at another. Machine Learning - a subset of Artificial Intelligence - with its well-documented ability to classify trends and patterns as well as ability to deal with multi-dimensional, multi-variable big data, is well-placed to: 1. help improve one's understanding of this situation's complexity 2. transform how new towns, cities, and large-scale mixed-use urban developments (LMUDs) are conceived, developed, and maintained, and 3. augment the performance outcomes of new towns, cities, and LMUDs.