Sustainability Assessment: An Adaptive Neuro-Fuzzy Inference System Approach
Urbanization especially in developing countries is a major driver for economic and social development.However,it has induced major concerns from past urbanization experiences,such as air pollution,traffic congestion,and habitat destruction.Such adverse effects caused by urbanization have generated greater pressures on governments to re-think their urban development policies to be sustainable ones.Within this context,various sustainability assessment methods have been developed by existing studies.Due to the dynamic features of sustainable development,fuzzy logic has been widely used for measuring sustainability performance.However,it is argued that most studies are using pre-defined simple linear membership functions and fuzzy rules which are mostly based on experts knowledge.The assessment results may not reflect the real sustainability performance.Therefore,there is a need to develop a new approach for induction of membership functions and fuzzy rules.This paper aims to introduce an adaptive neuro-fuzzy inference systems(ANFIS)approach for city level sustainability assessment.The membership functions and fuzzy rules are generated from 185 training samples.The results show that the new ranking of the selected 185 cities in China is close to the original with minor differences.It indicates that the new approach is valid and effective.
urban sustainability ANFIS fuzzy logic artificial neural network
TAN Yongtao SHUAI Chenyang JIAO Liudan SHEN Liyin
The Hong Kong Polytechnic University,Hong Kong SAR The Hong Kong Polytechnic University,Hong Kong SAR;Chongqing University,Mainland China Chongqing Jiao Tong University,Mainland China Chongqing University,Mainland China
国际会议
2017世界可持续建筑环境大会(the World Sustainable Built Environment Conference)
香港
英文
1037-1041
2017-06-05（万方平台首次上网日期，不代表论文的发表时间）