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Smart city urban planning using an evolutionary deep learning model

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Mansoor Alghamdi at University of Tabuk
Mansoor Alghamdi
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Following the evolution of big data collection, storage, and manipulation techniques, deep learning has drawn the attention of numerous recent studies proposing solutions for smart cities. These solutions were focusing especially on energy consumption, pollution levels, public services, and traffic management issues. Predicting urban evolution and planning is another recent concern for smart cities. In this context, this paper introduces a hybrid model that incorporates evolutionary optimization algorithms, such as Teaching–learning-based optimization (TLBO), into the functioning process of neural deep learning models, such as recurrent neural network (RNN) networks. According to the achieved simulations, deep learning enhanced by evolutionary optimizers can be an effective and promising method for predicting urban evolution of future smart cities.
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APPLICATION OF SOFT COMPUTING
Smart city urban planning using an evolutionary deep learning model
Mansoor Alghamdi
1
Accepted: 4 April 2023 / Published online: 18 April 2023
ÓThe Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
Abstract
Following the evolution of big data collection, storage, and manipulation techniques, deep learning has drawn the attention
of numerous recent studies proposing solutions for smart cities. These solutions were focusing especially on energy
consumption, pollution levels, public services, and traffic management issues. Predicting urban evolution and planning is
another recent concern for smart cities. In this context, this paper introduces a hybrid model that incorporates evolutionary
optimization algorithms, such as Teaching–learning-based optimization (TLBO), into the functioning process of neural
deep learning models, such as recurrent neural network (RNN) networks. According to the achieved simulations, deep
learning enhanced by evolutionary optimizers can be an effective and promising method for predicting urban evolution of
future smart cities.
Keywords Deep learning Multi-objective optimization RNN Smart cities TLBO Urban planification
Abbreviations
AI Artificial intelligence
ANN Artificial neural network
DL Deep learning
EANN Evolutionary artificial neural networks
EKF Evolutionary Kalman filter
GA Genetic algorithm
GANN Genetic artificial neural networks
IoT Internet of Things
LSTM Short-term memory
MAE Mean absolute error
MAPE Mean absolute percentage error
ML Machine learning
NN Neural network
RMSE Root mean square error
RNN Recurrent neural networks
SAE Staked auto-encoder
SC Smart city
TLBO Teaching–learning-based algorithm
WNN Wavelet neural networks
WOA Whale optimization algorithm
1 Introduction
The urban population continues doubling throughout the
world. In 2007, for the first time, the number of people
living in cities exceeded the number of people living in
rural areas. In 2030, the urban population will be about
60% of the world’s population (World Bank 2022).
This urban evolution poses several economic, social,
and environmental challenges, such as non-resilience,
pollution, non-sustainability, and congestion. This makes
smart cities a necessity in the future. Thus, planning and
predicting the urban evolution of the city is vital for smart
cities to meet several technological security and connec-
tivity requirements to optimize the living experience of the
residents.
At the same time, information technology is involved in
different aspects of our lives. The concept of smart cities is
possible due to the increased use of Internet of Things
(IoT) connected objects, making data collection easier
(Mnasri et al. 2018). Smart cities combine several recent
technologies, especially artificial intelligence (AI), to
optimize numerous issues, such as planning the urban
evolution, managing the energy consumption, controlling
the pollution levels, and estimating the road traffic (Cal-
zada et al. 2021). Several cities worldwide, such as Rome,
Paris, Boston, and Tokyo, have started implementing these
technologies.
However, many issues and challenges arise when plan-
ning the evolution of the structure of a smart city. For
&Mansoor Alghamdi
malghamdi@ut.edu.sa
1
Department of Computer Science, Applied College,
University of Tabuk, Tabuk, Saudi Arabia
123
Soft Computing (2024) 28:447–459
https://doi.org/10.1007/s00500-023-08219-4(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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