论文标题
对智能城市和城市可持续性的深度学习和机器学习模型的艺术调查
State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability
论文作者
论文摘要
深度学习（DL）和机器学习（ML）方法最近在预测，计划和不确定性分析的智能城市和城市发展的各个方面都为模型的发展做出了贡献。本文介绍了该领域中使用的DL和ML方法的艺术状态。通过新颖的分类法，提出了模型开发和城市可持续性和智能城市的新应用领域的进步。研究结果表明，五种DL和ML方法最适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；合奏，贝叶斯人，杂种和神经模糊；和深度学习。还披露，能源，健康和城市运输是DL和ML方法为解决其问题做出贡献的智能城市的主要领域。
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.
