Green roofs and their effect on architectural design and urban ecology using deep learning approaches

In recent years, the rapid development of the world’s economy has led to the large-scale development and utilization of ecological resources on the earth, due to which the ecological environment has been continuously and seriously damaged, resulting in the waste of resources, soil erosion, land dese...

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Veröffentlicht in:Soft computing (Berlin, Germany) Germany), 2024-02, Vol.28 (4), p.3667-3682
Hauptverfasser: Wang, Chongyu, Guo, Jiayin, Liu, Juan
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Liu, Juan
description In recent years, the rapid development of the world’s economy has led to the large-scale development and utilization of ecological resources on the earth, due to which the ecological environment has been continuously and seriously damaged, resulting in the waste of resources, soil erosion, land desertification, etc. To avoid further damage to the ecological environment and ecological resources, improve the utilization rate of ecological resources, and ensure the sustainable development of human society, it is necessary to evaluate the ecological environment. In this study, we collected the required data using the Delphi method and remote sensing technology. Secondly, the green Olympic building evaluation system (which refers to the CASBEE method in Japan) was used to evaluate the impact of green roofs on architectural design and the urban ecological environment. Third, a deep learning (DL)-based hybrid model, which consists of a convolutional neural network (CNN) and long–short-term memory (SLSTM), known as CNN–LSTM, was used to evaluate the impact of green roofs on urban ecology and building architectural design. The influence of thermal comfort on the indoor environment of green roof buildings was studied. For experimentation, six samples of Shanghai Thumb Plaza, Splendid Tesco Point, Chaoshan Yuan Hotel, Green Management Office, Huangpu District Domestic Waste Transfer Station, and Changning District Fuxin Slaughterhouse were selected as evaluation objects, and the effect of green roofs on building design and urban ecology was evaluated from six levels: ecological, ornamental, safety, functional, social, and economic. Both the CASBEE and DL-based methods, CNN–LSTM, performed well and increased the evaluation results to some extent. The CNN–LSTM model increased the accuracy of the system by 3.55%, precision by 3.50%, recall by 4.46%, and F1-score by 3.30%. Overall, this study summarizes the existing problems of green rooftop buildings in Shanghai at this stage, which is conducive to formulating optimization strategies to improve the ecological benefits of green roof buildings and has important practical significance for realizing the sustainable development of human society.
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subjects Abattoirs
Air pollution
Application of Soft Computing
Artificial Intelligence
Artificial neural networks
Building design
Climate change
Computational Intelligence
Control
Damage
Deep learning
Delphi method
Desertification
Ecological effects
Ecology
Economic development
Engineering
Green buildings
Green roofs
Household wastes
Indoor environments
Machine learning
Mathematical Logic and Foundations
Mechatronics
Model accuracy
Outdoor air quality
Remote sensing
Robotics
Roofs
Soil erosion
Sustainable development
Thermal comfort
Transfer stations
Urban environments
Urbanization
Water shortages
title Green roofs and their effect on architectural design and urban ecology using deep learning approaches
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