深度学习和机器学习模型的研究现状 智慧城市与城市可持续性

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摘要翻译：
深度学习(DL)和机器学习(ML)方法近年来在智慧城市和城市发展的预测、规划和不确定性分析等各个方面为模型的进步做出了贡献。本文介绍了DL和ML方法在这一领域的应用现状。通过一个新的分类法，介绍了城市可持续发展和智慧城市模型开发的进展和新的应用领域。研究结果表明，五种DL和ML方法被最多地应用于解决智能城市的不同方面。这些是人工神经网络；支持向量机；决策树；集合、贝叶斯、杂交和神经模糊；和深度学习。还揭示了能源、健康和城市交通是DL和ML方法用于解决其问题的智能城市的主要领域。
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英文标题：
《State of the Art Survey of Deep Learning and Machine Learning Models for
  Smart Cities and Urban Sustainability》
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作者：
Saeed Nosratabadi, Amir Mosavi, Ramin Keivani, Sina Ardabili, and
  Farshid Aram
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.
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PDF链接：
https://arxiv.org/pdf/2010.02670

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关键词：深度学习 可持续性 研究现状 智慧城市 机器学习 发展 learning methods domains 应用