State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

  • Saeed Nosratabadi
  • Amir MosaviEmail author
  • Ramin Keivani
  • Sina Ardabili
  • Farshid Aram
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)


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.


Deep learning Machine learning Smart cities Urban sustainability Cities of future Internet of things (IoT) Data science Big data 


  1. 1.
    Haase, D., et al. Global Urbanization. In: The Urban Planet: Knowledge Towards Sustainable Cities, vol. 19 (2018)Google Scholar
  2. 2.
    Galea, S., Ettman, C.K., Vlahov, D.: The Present and Future of Cities. Urban Health, p. 1 (2019)CrossRefGoogle Scholar
  3. 3.
    Wang, S.J., Moriarty, P.: Urban health and well-being challenges. In: Big Data for Urban Sustainability, pp. 23–43. Springer (2018)Google Scholar
  4. 4.
    Nosratabadi, S., et al.: Sustainable business models: a review. Sustainability 11(6), 1663 (2019)CrossRefGoogle Scholar
  5. 5.
    Alavi, A.H., et al.: Internet of Things-enabled smart cities: state-of-the-art and future trends. Measurement 129, 589–606 (2018)CrossRefGoogle Scholar
  6. 6.
    Dineva, A., et al.: Review of soft computing models in design and control of rotating electrical machines. Energies 12(6) (2019)CrossRefGoogle Scholar
  7. 7.
    Ghalandari, M., et al.: Investigation of submerged structures’ flexibility on sloshing frequency using a boundary element method and finite element analysis. Eng. Appl. Comput. Fluid Mech. 13(1), 519–528 (2019)Google Scholar
  8. 8.
    Qasem, S.N., et al.: Estimating daily dew point temperature using machine learning algorithms. Water (Switzerland) 11(3) (2019)CrossRefGoogle Scholar
  9. 9.
    Taherei Ghazvinei, P., et al.: Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Eng. Appl. Comput. Fluid Mech. 12(1), 738–749 (2018)Google Scholar
  10. 10.
    Vargas, R., Mosavi, A., Ruiz, R.: Deep learning: a review. In: Advances in Intelligent Systems and Computing (2017)Google Scholar
  11. 11.
    Mosavi, A., Lopez, A., Várkonyi-Kóczy, A.R.: Industrial applications of big data: state of the art survey. In: Luca, D., Sirghi, L., Costin, C. (eds.), pp. 225–232. Springer (2018)Google Scholar
  12. 12.
    Mosavi, A., Ozturk, P., Chau, K.W.: Flood prediction using machine learning models: literature review. Water (Switzerland) 10(11) (2018)CrossRefGoogle Scholar
  13. 13.
    Mosavi, A,. Rabczuk, T.: Learning and intelligent optimization for material design innovation. In: Kvasov, D.E., et al. (eds.), pp. 358–363. Springer (2017)Google Scholar
  14. 14.
    Mosavi, A., Várkonyi-Kóczy, A.R.: Integration of machine learning and optimization for robot learning. In: Jablonski, R., Szewczyk, R. (eds.), pp. 349–355. Springer (2017)Google Scholar
  15. 15.
    Torabi, M., et al.: A Hybrid clustering and classification technique for forecasting short-term energy consumption. Environ. Prog. Sustain. Energy 38(1), 66–76 (2019)CrossRefGoogle Scholar
  16. 16.
    Audu, A.R.A., et al.: An intelligent predictive analytics system for transportation analytics on open data towards the development of a smart city. In: Hussain, F.K., Barolli, L., Ikeda, M. (eds.) pp. 224–236. Springer (2020)Google Scholar
  17. 17.
    Rebelo, F., Noriega, P., Oliveira, T.: Evaluation of the concept of a smart city gamification from a user centered design perspective. In: Soares, M.M., Rebelo, F. (eds.), pp. 207–219. Springer (2020)Google Scholar
  18. 18.
    Ruzina, E.I.: From information city to smart city: Russian experience of state entrepreneurship. In: Solovev, D.B. (ed.), pp. 419–430. Springer Science and Business Media Deutschland GmbH (2020)Google Scholar
  19. 19.
    Sharifi, A.: A critical review of selected smart city assessment tools and indicator sets. J. Clean. Prod. 233, 1269–1283 (2019)CrossRefGoogle Scholar
  20. 20.
    Valdeolmillos, D., Mezquita, Y., Ludeiro, A.R.: Sensing as a service: An architecture proposal for big data environments in smart cities. In: Novais, P., et al. (eds.), pp. 97–104. Springer (2020)Google Scholar
  21. 21.
    Wataya, E., Shaw, R.: Measuring the value and the role of soft assets in smart city development. Cities 94, 106–115 (2019)CrossRefGoogle Scholar
  22. 22.
    Aram, F., et al.: Design and validation of a computational program for analysing mental maps: Aram mental map analyzer. Sustainability (Switzerland) 11(14) (2019)CrossRefGoogle Scholar
  23. 23.
    Asadi, E., et al.: Groundwater quality assessment for drinking and agricultural purposes in Tabriz Aquifer, Iran (2019)Google Scholar
  24. 24.
    Asghar, M.Z., Subhan, F., Imran, M., Kundi, F.M., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Performance evaluation of supervised machine learning techniques for efficient detection of emotions from online content (2019), 2019080019.
  25. 25.
    Bemani, A., Baghban, A., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Applying ANN, ANFIS, and LSSVM models for estimation of acid solvent solubility in supercritical CO2. (2019), 2019060055.
  26. 26.
    Choubin, B., et al.: Snow avalanche hazard prediction using machine learning methods. J. Hydrol. 577 (2019)CrossRefGoogle Scholar
  27. 27.
    Choubin, B., et al.: An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci. Total Environ. 651, 2087–2096 (2019)CrossRefGoogle Scholar
  28. 28.
    Dehghani, M., et al.: Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system. Energies 12(2) (2019)CrossRefGoogle Scholar
  29. 29.
    Dineva, A., et al.: Multi-label classification for fault diagnosis of rotating electrical machines (2019)Google Scholar
  30. 30.
    Farzaneh-Gord, M., et al.: Numerical simulation of pressure pulsation effects of a snubber in a CNG station for increasing measurement accuracy. Eng. Appl. Comput. Fluid Mech. 13(1), 642–663 (2019)Google Scholar
  31. 31.
    Ghalandari, M., et al.: Flutter speed estimation using presented differential quadrature method formulation. Eng. Appl. Comput. Fluid Mech. 13(1), 804–810 (2019)Google Scholar
  32. 32.
    Karballaeezadeh, N., et al.: Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road). Eng. Appl. Comput. Fluid Mech. 13(1), 188–198 (2019)Google Scholar
  33. 33.
    Menad, N.A., et al.: Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming. Eng. Appl. Comput. Fluid Mech. 13(1), 724–743 (2019)MathSciNetGoogle Scholar
  34. 34.
    Mohammadzadeh, S., et al.: Prediction of compression index of fine-grained soils using a gene expression programming model. Infrastructures 4(2), 26 (2019)CrossRefGoogle Scholar
  35. 35.
    Mosavi, A., Edalatifar, M.: A hybrid neuro-fuzzy algorithm for prediction of reference evapotranspiration. in Lecture Notes in Networks and Systems, pp. 235–243. Springer (2019)Google Scholar
  36. 36.
    Mosavi, A., Rabczuk, T., Várkonyi-Kóczy, A.R.: Reviewing the novel machine learning tools for materials design. In: Luca, D., Sirghi, L., Costin, C. (eds.), pp. 50–58. Springer (2018)Google Scholar
  37. 37.
    Mosavi, A., et al.: State of the art of machine learning models in energy systems, a systematic review. Energies 12(7) (2019)CrossRefGoogle Scholar
  38. 38.
    Mosavi, A., et al.: Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Eng. Appl. Comput. Fluid Mech. 13(1), 482–492 (2019)Google Scholar
  39. 39.
    Nosratabadi, S., et al.: Sustainable business models: a review. Sustainability (Switzerland) 11(6) (2019)CrossRefGoogle Scholar
  40. 40.
    Rezakazemi, M., Mosavi, A., Shirazian, S.: ANFIS pattern for molecular membranes separation optimization. J. Mol. Liq. 274, 470–476 (2019)CrossRefGoogle Scholar
  41. 41.
    Riahi-Madvar, H., et al.: Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry. Eng. Appl. Comput. Fluid Mech. 13(1), 529–550 (2019)Google Scholar
  42. 42.
    Shabani, S., Samadianfard, S., Taghi Sattari, M., Shamshirband, S., Mosavi, A., Kmet, T., Várkonyi-Kóczy, A.R.: Modeling daily pan evaporation in humid cli-mates using gaussian process regression (2019), 2019070351.
  43. 43.
    Shamshirband, S., Hadipoor, M., Baghban, A., Mosavi, A., Bukor J., Várkonyi-Kóczy, A.R.: Developing an ANFIS-PSO model to predict mercury emissions in combustion flue gases (2019), 2019070165.
  44. 44.
    Shamshirband, S., et al.: Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng. App. Comput. Fluid Mech. 13(1), 91–101 (2019)Google Scholar
  45. 45.
    Shamshirband, S., Mosavi, A., Rabczuk, T.: Particle swarm optimization model to predict scour depth around bridge pier (2019). arXiv:1906.08863
  46. 46.
    Torabi, M., et al.: A hybrid machine learning approach for daily prediction of solar radiation. In: Lecture Notes in Networks and Systems, pp. 266–274. Springer (2019)Google Scholar
  47. 47.
    de Souza, J.T., et al.: Data mining and machine learning to promote smart cities: a systematic review from 2000 to 2018. Sustainability (Switzerland) 11(4) (2019)CrossRefGoogle Scholar
  48. 48.
    Muhammed, T., et al.: UbeHealth: A personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 6, 32258–32285 (2018)CrossRefGoogle Scholar
  49. 49.
    Nagy, A.M., Simon, V.: Survey on traffic prediction in smart cities. Pervasive Mobile Comput. 50, 148–163 (2018)CrossRefGoogle Scholar
  50. 50.
    O’Dwyer, E., et al.: Smart energy systems for sustainable smart cities: current developments, trends and future directions. Appl. Energy 581–597 (2019)CrossRefGoogle Scholar
  51. 51.
    Soomro, K., et al.: Smart city big data analytics: An advanced review. In: Data Mining and Knowledge Discovery. Wiley Interdisciplinary Reviews (2019)Google Scholar
  52. 52.
    Usman, M., et al.: A survey on big multimedia data processing and management in smart cities. ACM Comput. Surv. 52(3) (2019)CrossRefGoogle Scholar
  53. 53.
    Zhao, L., et al.: Routing for crowd management in smart cities: A deep reinforcement learning perspective. IEEE Commun. Mag. 57(4), 88–93 (2019)CrossRefGoogle Scholar
  54. 54.
    Aram, F., et al.: Design and validation of a computational program for analysing mental maps: aram mental map analyzer. Sustainability 11(14), 3790 (2019)CrossRefGoogle Scholar
  55. 55.
    Ullah, I., et al.: Smart lightning detection system for smart-city infrastructure using artificial neural network. Wirel. Pers. Commun. 106(4), 1743–1766 (2019)CrossRefGoogle Scholar
  56. 56.
    Yuan, Z., Wang, W., Fan, X.: Back propagation neural network clustering architecture for stability enhancement and harmonic suppression in wind turbines for smart cities. Comput. Electr. Eng. 74, 105–116 (2019)CrossRefGoogle Scholar
  57. 57.
    Rojek, I., Studzinski, J.: Detection and localization of water leaks in water nets supported by an ICT system with artificial intelligence methods as away forward for smart cities. Sustainability (Switzerland) 11(2) (2019)CrossRefGoogle Scholar
  58. 58.
    Pan, X., et al.: Prediction of network traffic of smart cities based on DE-BP neural network. IEEE Access 7, 55807–55816 (2019)CrossRefGoogle Scholar
  59. 59.
    Vlahogianni, E.I., et al.: A real-time parking prediction system for smart cities. J. Intell. Trans. Syst. Technol. Plan. Oper. 20(2), 192–204 (2016)CrossRefGoogle Scholar
  60. 60.
    Livingston, S.J., et al.: A hybrid approach for water utilization in smart cities using machine learning techniques. Int. J. Innov. Technol. Explor. Eng. 8(6), 488–493 (2019)Google Scholar
  61. 61.
    Chen, L., Zhang, H.: Evaluation of green smart cities in china based on entropy weight-cloud model. Xitong Fangzhen Xuebao/J Syst Simul. 31(1), 136–144 (2019)Google Scholar
  62. 62.
    Chui, K.T., Lytras, M.D., Visvizi, A.: Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 11(11) (2018)CrossRefGoogle Scholar
  63. 63.
    Aborokbah, M.M., et al.: Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis. Sustain. Cities Soc. 41, 919–924 (2018)CrossRefGoogle Scholar
  64. 64.
    Muhammad, G., et al.: A facial-expression monitoring system for improved healthcare in smart cities. IEEE Access 5, 10871–10881 (2017)CrossRefGoogle Scholar
  65. 65.
    Ilapakurti, A., et al.: Adaptive edge analytics for creating memorable customer experience and venue brand engagement, a scented case for Smart Cities. Institute of Electrical and Electronics Engineers Inc. (2018)Google Scholar
  66. 66.
    Orlowski, C., et al.: Decision processes based on IoT data for sustainable smart cities. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 136–146. Springer (2018)Google Scholar
  67. 67.
    Mei, H., Poslad, S., Du, S.: A game-theory based incentive framework for an intelligent traffic system as part of a smart city initiative. Sensors (Switzerland) 17(12) (2017)CrossRefGoogle Scholar
  68. 68.
    Vuppalapati, J.S., et al.: Smart dairies-enablement of smart city at gross root level. Institute of Electrical and Electronics Engineers Inc. (2017)Google Scholar
  69. 69.
    Nguyen, T.A., et al.: Toward a sustainable city of tomorrow: a hybrid Markov-Cellular Automata modeling for urban landscape evolution in the Hanoi city (Vietnam) during 1990–2030. Environ. Dev. Sustain. 21(1), 429–446 (2019)CrossRefGoogle Scholar
  70. 70.
    Taveres-Cachat, E., et al.: Responsive building envelope concepts in zero emission neighborhoods and smart cities-a roadmap to implementation. Build. Environ. 149, 446–457 (2019)CrossRefGoogle Scholar
  71. 71.
    Ju, J., Liu, L., Feng, Y.: Citizen-centered big data analysis-driven governance intelligence framework for smart cities. Telecommun. Policy 42(10), 881–896 (2018)CrossRefGoogle Scholar
  72. 72.
    Tan, Y., et al.: Adaptive neuro-fuzzy inference system approach for urban sustainability assessment: a China case study. Sustain. Dev. 26(6), 749–764 (2018)CrossRefGoogle Scholar
  73. 73.
    Sajjad, M., et al.: Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE Access 5, 3475–3489 (2017)CrossRefGoogle Scholar
  74. 74.
    Luo, H., et al.: A short-term energy prediction system based on edge computing for smart city. Future Gener. Comput. Syst. 101, 444–457 (2019)CrossRefGoogle Scholar
  75. 75.
    Vázquez-Canteli, J.R., et al.: Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities. Sustain. Cities Soc. 45, 243–257 (2019)CrossRefGoogle Scholar
  76. 76.
    Baba, M., et al.: A sensor network approach for violence detection in smart cities using deep learning. Sensors (Switzerland) 19(7) (2019)CrossRefGoogle Scholar
  77. 77.
    Reddy, D.V.S., Mehta, R.V.K.: Smart traffic management system for smart cities using reinforcement learning algorithm. Int. J. Recent Technol. Eng. 7(6), 12–15 (2019)Google Scholar
  78. 78.
    Obinikpo, A.A., Kantarci, B.: Big sensed data meets deep learning for smarter health care in smart cities. J. Sens. Actuator Netw. 6(4) (2017)CrossRefGoogle Scholar
  79. 79.
    Madu, C.N., Kuei, C.H., Lee, P.: Urban sustainability management: A deep learning perspective. Sustain. Cities Soc. 30, 1–17 (2017)CrossRefGoogle Scholar
  80. 80.
    Ardabili, S., Mosavi, A., Mahmoudi, Gundoshmian, T.M., Nosratabadi, S., Varkonyi-Koczy, A.: Modelling temperature variation of mushroom growing hall using artificial neural networks (2019)Google Scholar
  81. 81.
    Gundoshmian, T.M., Ardabili, S., Mosavi, A., Varkonyi-Koczy, A., Prediction of combine harvester performance using hybrid machine learning modeling and response surface methodology (2019)Google Scholar
  82. 82.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Systematic review of deep learning and machine learning models in biofuels research (2019)Google Scholar
  83. 83.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A., Advances in machine learning model-ing reviewing hybrid and ensemble methods (2019)Google Scholar
  84. 84.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Building Energy information: demand and consumption prediction with Machine Learning models for sustainable and smart cities (2019)Google Scholar
  85. 85.
    Ardabili, S., Mosavi, A., Dehghani, M., Varkonyi-Koczy, A.: Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review (2019)Google Scholar
  86. 86.
    Mohammadzadeh D., Karballaeezadeh, N., Mohemmi, M., Mosavi, A., Várkonyi-Kóczy A.: Urban train soil-structure interaction modeling and analysis (2019)Google Scholar
  87. 87.
    Mosavi, A., Ardabili, S., Varkonyi-Koczy, A., List of deep learning models (2019)Google Scholar
  88. 88.
    Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S., Aram, F.: State of the art survey of deep learning and machine learning models for smart cities and urban sustainability (2019)Google Scholar

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Authors and Affiliations

  1. 1.Institute of Business Studies, Szent Istvan UniversityGodolloHungary
  2. 2.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  3. 3.Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  4. 4.Institute of Advanced Studies KoszegKoszegHungary
  5. 5.Escuela Técnica Superior de Arquitectura, Universidad Politécnica de Madrid-UPMMadridSpain

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