Abstract
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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References
Asadi A, Arefi H, Fathipoor H (2020) Simulation of green roofs and their potential mitigating effects on the urban heat island using an artificial neural network: a case study in Austin, Texas. Adv Space Res 66:1846–1862
Cascone S, Coma J, Gagliano A, Pérez G (2019) The evapotranspiration process in green roofs: a review. Build Environ 147:337–355
Chun B, Guldmann J-M (2018) Impact of greening on the urban heat island: seasonal variations and mitigation strategies. Comput Environ Urban Syst 71:165–176
Dimitrijevic-Jovanovic D, Zivkovic P, Stevanovic Z (2018) The impact of the building envelope with the green living systems on the built environment. Therm Sci 22:225–225
Dong J, Lin M, Zuo J, Lin T, Liu J, Sun C, Luo J (2020) Quantitative study on the cooling effect of green roofs in a high-density urban Area—a case study of Xiamen, China. J Clean Prod 255:120152
Erdemir D, Ayata T (2017) Prediction of temperature decreasing on a green roof by using artificial neural network. Appl Therm Eng 112:1317–1325
Fu Q, Li Z, Ding Z, Chen J, Luo J, Wang Y, Lu Y (2023) ED-DQN: an event-driven deep reinforcement learning control method for multi-zone residential buildings. Build Environ 242:110546
Gauch M, Kratzert F, Klotz D, Nearing G, Lin J, Hochreiter S (2021) Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network. Hydrol Earth Syst Sci 25:2045–2062
Gu C (2019) Urbanization: processes and driving forces. Sci China Earth Sci 62:1351–1360
Guo L-H, Cheng S, Liu J, Wang Y, Cai Y, Hong X-C (2022) Does social perception data express the spatio-temporal pattern of perceived urban noise? A case study based on 3,137 noise complaints in Fuzhou, China. Appl Acoust 201:109129
Hong T, Wu X, Chen Y, Lin X (2018) Impact of roof greening on the ecological environment of the green building, exemplified by the roof garden of the Mingde Building in Fujian Agricultural and Forestry University
Hu C, Wu Q, Li H, Jian S, Li N, Lou Z (2018) Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water 10:1543
Kratzert F, Klotz D, Shalev G, Klambauer G, Hochreiter S, Nearing G (2019) Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol Earth Syst Sci 23:5089–5110
Lees T, Buechel M, Anderson B, Slater L, Reece S, Coxon G, Dadson SJ (2021) Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models. Hydrol Earth Syst Sci 25:5517–5534
Li S, Kazemi H, Rockaway TD (2019) Performance assessment of stormwater GI practices using artificial neural networks. Sci Total Environ 651:2811–2819
Li W, Kiaghadi A, Dawson C (2021) Exploring the best sequence LSTM modeling architecture for flood prediction. Neural Comput Appl 33:5571–5580
Li B, Gao J, Chen S, Lim S, Jiang H (2022) POI detection of high-rise buildings using remote sensing images: a semantic segmentation method based on multitask attention Res-U-Net. IEEE Trans Geosci Remote Sens 60:1–16
Li X, Wang F, Al-Razgan M, Awwad EM, Abduvaxitovna SZ, Li Z, Li J (2023) Race to environmental sustainability: can structural change, economic expansion and natural resource consumption effect environmental sustainability? A novel dynamic ARDL simulations approach. Resour Policy 86:104044
Liu X, Li Z, Fu X, Yin Z, Liu M, Yin L, Zheng W (2023) Monitoring house vacancy dynamics in the pearl river delta region: a method based on NPP-viirs night-time light remote sensing images. Land 12:831
Luo J, Wang Y, Li G (2023) The innovation effect of administrative hierarchy on intercity connection: the machine learning of twin cities. J Innov Knowl 8:100293
Ma Q, Li Y, Xu L (2021) Identification of green infrastructure networks based on ecosystem services in a rapidly urbanizing area. J Clean Prod 300:126945
Ma M, Tam VW, Le KN, Butera A, Li W, Wang X (2023) Comparative analysis on international construction and demolition waste management policies and laws for policy makers in China. J Civ Eng Manag 29:107–130
Mousavi S, Gheibi M, Wacławek S, Behzadian K (2023) A novel smart framework for optimal design of green roofs in buildings conforming with energy conservation and thermal comfort. Energy Build 291:113111
Muhammad Y, Hassan MA, Almotairi S, Farooq K, Granelli F, Strážovská Ľ (2023) The role of socioeconomic factors in improving the performance of students based on intelligent computational approaches. Electronics 12:1982
Peng LH, Yang XS, Qian J, Zhu CL, Yao LY, Jiang ZD (2017) Green-roof effects on urban microclimate and stormwater runoff. Resour Environ Yangtze Basin 26(10):1658–1667
Raji B, Tenpierik MJ, Andy V (2015) The impact of greening systems on building energy performance: a literature review. Renew Sustain Energy Rev 45:610–623
Shang M, Luo J (2021) The tapio decoupling principle and key strategies for changing factors of Chinese urban carbon footprint based on cloud computing. Int J Environ Res Public Health 18:2101
Sims AW, Robinson CE, Smart CC, O’Carroll DM (2019) Mechanisms controlling green roof peak flow rate attenuation. J Hydrol 577:123972
Singh A, Wang Y, Zhou Y, Sun J, Xu X, Li Y, Liu Z, Chen J, Wang X (2023) Utilization of antimony tailings in fiber-reinforced 3D printed concrete: a sustainable approach for construction materials. Constr Build Mater 408:133689
Song S, Liu Z, He C, Lu W (2020) Evaluating the effects of urban expansion on natural habitat quality by coupling localized shared socioeconomic pathways and the land use scenario dynamics-urban model. Ecol Indic 112:106071
Tsang S, Jim CY (2016) Applying artificial intelligence modeling to optimize green roof irrigation. Energy Build 127:360–369
Wang K, Li Z, Zhang J, Wu X, Jia M, Wu L (2020) Built-up land expansion and its impacts on optimizing green infrastructure networks in a resource-dependent city. Sustain Cities Soc 55:102026
Wang J, Mei GX, Huang S, Huang DS, Liu JQ (2021) Review of hydrology and environmental benefits of green roof technology based on sponge city construction. J China Hydrol 41(1):42–48
Xie H, Randall M, Chau K-W (2022) Green roof hydrological modelling with GRU and LSTM networks. Water Resour Manag 36:1107–1122
Yan HY, Qiao JS (2011) Survey research of roof greening based on public awareness in Jiaozuo. North Hortic 3:106–112
Young C-C, Liu W-C, Wu M-C (2017) A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Appl Soft Comput 53:205–216
Yu C, Li J, Song S, Zou L, University NA (2017) A study of outdoor thermal environment of roof greening in Nanjing. J Chin Urban For
Zhang G, He B-J, Zhu Z, Dewancker BJ (2019) Impact of morphological characteristics of green roofs on pedestrian cooling in subtropical climates. Int J Environ Res Public Health 16:179
Zhang R, Zhang L, Zhong Q, Zhang Q, Ji Y, Song P, Wang Q (2021) An optimized evaluation method of an urban ecological network: the case of the Minhang District of Shanghai. Urban for Urban Green 62:127158
Zhang P, Liu L, Yang L, Zhao J, Li Y, Qi Y, Ma X, Cao L (2023) Exploring the response of ecosystem service value to land use changes under multiple scenarios coupling a mixed-cell cellular automata model and system dynamics model in Xi’an, China. Ecol Indic 147:110009
Zhao M, Zhou Y, Li X, Cheng W, Zhou C, Ma T, Li M, Huang K (2020) Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sens Environ 248:111980
Zhao R, Huang X, Xue J, Guan X (2023) A practical simulation of carbon sink calculation for urban buildings: a case study of Zhengzhou in China. Sustain Cities Soc 99:104980
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Wang, C., Guo, J. & Liu, J. Green roofs and their effect on architectural design and urban ecology using deep learning approaches. Soft Comput 28, 3667–3682 (2024). https://doi.org/10.1007/s00500-024-09637-8
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DOI: https://doi.org/10.1007/s00500-024-09637-8