Evaluation of Metaheuristic Algorithms in Selecting the Building Optimal Cover Based on the Effect of Urban Heat Islands

Ghandian, A.; Mostofi, Ni.; Majidizadeh, A.; Motieyan, H.

doi:10.22061/jrsgr.2024.10709.1056

Document Type : Original Research Paper

Authors

¹ Department of Geomatics Engineering, Faculty of Engineering, Islamic Azad University- South Tehran Branch, Tehran, Iran

² Department of Geodesy and Surveying Engineering, Faculty of Engineering, Tafresh University, Tafresh, Iran

³ Department of Geomatics Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Balbo, Iran

https://doi.org/10.22061/jrsgr.2024.10709.1056

Abstract

Background and Objectives: Nowadays, the development of urbanization and the increase of urban population have caused the air to heat up more than in the past and create urban heat islands. Urban heat islands are a phenomenon caused by the urbanization effects, due to which the temperature in the urban environment rises higher than in the suburbs. This phenomenon can cause irreparable damage due to the increasing atmospheric and environmental temperature, such as biological pollution, greenhouse gas emissions, diseases caused by heat, and impact on water quality brought to communities and the environment. This research proposes an effective and efficient approach with the help of remote sensing and optimization algorithms based on replacing the roof covering of an area with less heat-absorbing coverings to reduce the temperature and try to eliminate the heat island phenomenon. In this research, we are trying to reduce the urban heat island effect based on algorithms and statistical parameters affecting the ambient temperature, which has had few studies in past research. Also, using the intelligent optimization method in this field can cause innovation and create better and more accurate results. The new way that this study examines is to change the roof covering of an area with other functional coverings that reduce the air temperature in that area. The coverings that we considered to replace the covering of the roofs to moderate and cool the temperature of the studied area are two types of coverings: soil and vegetation.
Methods: The proposed approach of this research is to use two optimization algorithms of genetic and particle swarm, and the parameters that form the objective function of these two algorithms are the temperature standard deviation and the average financial cost of the coverage changing of each building parcel. The research dataset is Landsat 8 satellite images of Andisheh neighborhood in Tehran. This research uses satellite images for purposes such as preparing color images, mapping the vegetation and non-vegetation indices of the study area, and calculating the earth's surface temperature and urban heat islands.
Findings: The results indicate that both optimization algorithms have provided good performance and improved the problem parameters, but the genetic optimization algorithm obtained a better result in less time and iteration. In comparing the two algorithms, the genetic optimization algorithm reduced the standard deviation by 19%, bringing its value to 0.42. On the other hand, the particle swarm optimization algorithm for a longer time, reduced the standard deviation by 14%, bringing its value to 0.44.
Conclusion: The genetic algorithm in optimizing the building roofs obtained excellent results with a total cost of 4678 and a standard deviation of 0.4177. It converged quickly with the 12100 number of objective function evaluations and significantly reduced both the cost function parameters (The genetic algorithm has reached the best possible answer). The particle swarm optimization algorithm also failed to achieve an answer as good as the genetic algorithm with a total cost of 4965, a standard deviation of 0.4430, and a 20100 number of objective function evaluations. About the comparison between these two algorithms, the genetic, with less than 3000 objective function evaluations, was able to experience the most optimal solution that particle swarm algorithm reached with the 20100 number of function evaluations. The use of metaheuristic algorithms in practical problem optimizations, which we frequently encounter in various industries today, can be very efficient. The results of these algorithms are very suitable despite the differences in the outputs, and it will be impossible to reach such answers to different problems without using such algorithms. In future work, based on what we obtained in this research, we suggest using other optimization algorithms or even powerful modeling algorithms such as artificial neural networks. Also, it is possible to study the change in building roof covers and the use of newer coverings in moderating the temperature by adopting new parameters from the cost function in optimization and deep learning algorithms.

Keywords

Main Subjects

Remote Sensing

COPYRIGHTS
© 2024 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)

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