Estimation of metabolic flows of urban environment based on fuzzy expert knowledge
The quality and comfort of the urban environment serve as one of the most important factors for ensuring the competitiveness of municipalities, regions and countries. The quality of the urban environment is determined by the quality of its three components: anthropogenic, natural and social environment. The main problem of assessing the state of the urban environment is the fragmentation of methodological approaches and adequate tools for assessing the qualitative state of the urban environment. This objectively makes it difficult for municipal authorities to use this assessment as an element in the system of urban planning decision making. We have developed an intelligent information system to provide an assessment of potential, real and lost opportunities of the urban environment using fuzzy expert knowledge. This system operates in the conditions of using non-numeric, inaccurate and incomplete information to ensure the management of sustainable city development. The system for assessing the potential, real and lost opportunities of the urban environment is based on the use of fuzzy logic equations. It allows to evaluate the effectiveness of metabolic transformations of each subsystem of the urban environment.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Bettencour, L. A., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and pace of life in cities. Proceedings of the National Academy of Sciences, 104(17) 7301–7306. https://doi.org/10.1073/pnas.0610172104
Bolshakov, B. E., & Kuznetsov, O. L. (2010). Sustainable development: universal principle for the synthesis of natural, technical and social knowledge. Bulletin of the Russian Academy of Natural Sciences, 10(3), 3‒9.
Bolshakov, B. E., & Ryabkova, S. A. (2009). Origin and basic problems of entrance of the concept “Sustainable development” sn world policy and sciene attachment to the educational complex “Theory and methodology of sustainable design development of socio-natural systems”. Dubna.
Bolshakov, B. E. (2008). Theory and methodology of designing sustainable development of socio-natural systems. Teaching aid. Electronic edition (0220712064). Dubna. http://www.aup.ru/files/m536/m536.pdf
Butera, F., & Caputo, P. (2008). Planning eco-cities, the case of Huai Rou New Town. In Proceedings of the 3rd International Solar Cities Congress, Adelaide, Australia.
Caputo, P., Costa, G., & Manfren, M. (2018). Paradigm shift in urban energy systems through distributed generation. Applied Energy, 88(4), 1032–1048. https://doi.org/10.1016/j.apenergy.2010.10.018
Gerasimov, B. M., Divizinyuk, M. M., & Subach, I. Yu. (2004). Decision making systems: Design, application, performance evaluation. Monograph. Research Center for Armed Forces of Ukraine “State Oceanarium”.
Kapitsa, L. M. (2001). World development indicators. Phoenix.
Newman, P. (1989). Cities and automobile dependence: A source book. Gower.
Rotshtein, A. P. (1999). Intelligent identification technology. Universum-Vinnitsa.
Rotshtein, A. P. (1996). Medical diagnostics on fuzzy logic. Continent-Prim.
Tsvetkov, V. Y. (2016). Information models and geo-information models. International Journal of Applied and Basic Research. 3(15), 114–120. https://doi.org/10.21777/2312-5500-2016-4-114-120
Ursul, A. D. (2005). Sustainable development: Conceptual model. National Interests, (1).
Yager, R. R., & Filev, D. P. (1994). Essentials of fuzzy modelling and control. John Willey & Sons.