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Smart city urban planning using an evolutionary deep learning model

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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.

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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

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  1. Department of Computer Science, Applied College, University of Tabuk, Tabuk, Saudi Arabia

    Mansoor Alghamdi

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  1. Mansoor Alghamdi
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Alghamdi, M. Smart city urban planning using an evolutionary deep learning model. Soft Comput 28, 447–459 (2024). https://doi.org/10.1007/s00500-023-08219-4

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