Automatic design optimization of landscape space based on improved genetic algorithm in tropical environment

• Autorzy: Yu Liping

Identyfikatory

DOI

10.1007/s11600-022-00813-8

• Języki publikacji: EN

Abstrakty

EN

The problem of urban microclimate deterioration is becoming more and more serious, and the automatic optimization of landscape space in tropical and subtropical environment needs to be solved urgently. Urban development also leads to some problems in the urban environment which are related to people’s life and development. The main research content of this paper is the automatic optimization design of landscape space based on genetic algorithms and urban climate map. With the specific goal of improving the urban wind and heat environment, combined with the technical means in many fields such as architecture, urban planning, and computer technology, we try to establish a set of scientific automatic generation and optimization methods of urban design, which are suitable for different levels of work needs in the field of urban planning, from the mesoscale zoning planning to more microscopic neighborhood-scale urban design. The experimental results show that this paper uses genetic algorithms on the Rhino Grasshopper software platform to automatically find out the optimal layout of each modules with different proportions and distributions and generate the optimal layout of the environment for a given volume ratio.

Słowa kluczowe

EN

genetic algorithm; automatic optimization; urban landscape space; urban design; computer technology; urban planning; automatic generation

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 3
• Strony: 1475--1489
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Yu Liping

• Hainan Tropical Ocean University, Sanya, Hainan 572022, China

Bibliografia

• 1. Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT et al (2021) Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain India. Sci Total Environ 750:141565
• 2. Bady M, Kato S, Huang H (2008) Towards the application of indor ventilation efficiency indices to evaluate the air quality of urban areas. Build Environ 43(12):1991–2004
• 3. D R. Evolutionary Principles applied to Problem Solving using Galapagos. 2010: 2010.
• 4. Fan Z, Liu M, Tang S (2022) A multi-objective optimization design method for gymnasium facade shading ratio integrating Energy load and daylight comfort. Build Environ 207:108527
• 5. Pingan G (2014) Preliminary study on Urban climate map for humid and hot areas in the context of suitable rapid urbanization. South China University of Technology
• 6. Le-Thanh L, Le-Duc T, Ngo-Minh H, Nguyen QH, Nguyen-Xuan H (2021) Optimal design of an Origami-inspired kinetic façade by balancing composite motion optimization for improving daylight performance and energy efficiency. Energy 219:119557
• 7. Li X, Liu C, Leung D (2005) Development of a model for the determination of air exchange rates for street canyons. Atmos Environ 39(38):7285–7296
• 8. Li XF, Zhang ZQ, Lin BR et al (2003) Experimental study of microclimate in enclosed residential communities. J Tsinghua Univ (natural Sci Ed) 12:1638–1641
• 9. Lopes MD, da Silva GBL (2021) An efficient simulation-optimization approach based on genetic algorithms and hydrologic modeling to assist in identifying optimal low impact development designs. Landsc Urban Plan 216:104251
• 10. Ramponi R, Blocken B, de Coo LB et al (2015) CFD simulation of outdoor ventilation of generic urban configurations with different urban densities and equal and unequal street widths. Build Environ 92:152–166
• 11. Ren C, Wu E, Katschner B (2013) Application of Urban environmental climate information in German Urban planning and its implications. International Urban Planning. 2013(04):91–99
• 12. Ren C, Wu E (2012) Urban environmental climate map: an information system tool for sustainable urban planning. Construction Industry Press, Beijing
• 13. Skote M, Sandberg M, Westerberg U et al (2005) Numerical and experimental studies of wind environment in an urban morphology. Atmos Environ 39(33):6147–6158
• 14. Wu Y, Guo W, Yang D (2021) Application of neural network model based on multispecies evolutionary genetic algorithm to planning and design of diverse plant landscape. Comput Intell Neurosci
• 15. Zhu Y (2014) Research on the evaluation method of wind environment in Urban planning and design. Southeast University
• 16. Zhuang Z, Yu YB, Ye H et al (2014) Research status of CFD simulation technology for outdoor wind environment of buildings. Build Sci 02:108–11

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).