Adaptive neuro‐fuzzy inference system approach for urban sustainability assessment: A China case study

Abstract

Urbanization, especially in developing countries, has led to numerous concerns, such as air pollution, traffic congestion and habitat destruction. Within this context, it is important to evaluate urban development as sustainable, and various sustainability assessment methods have been developed, including fuzzy logic approaches. However, predefined fuzzy rules and simple linear membership functions were used, which are largely based on the knowledge of subject experts. Therefore, this paper aims to introduce an adaptive neuro‐fuzzy inference systems (ANFIS) approach for urban sustainability assessment. With collected training samples from the Urban China Initiative, and the ANFIS approach was used to rank 185 selected cities in China. The results show that the ANFIS approach is appropriate for assessing urban sustainability, and the nonlinear membership functions fit the training samples better than the linear membership functions. Further discussion indicates that future research on sustainability assessment should be more integrated.

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

“…Another ML method which has been applied to solve problems of different aspects of smart cities is Decision trees (DTs) method. As it is detailed in [72] develop an adaptive neuro-fuzzy inference system approach for urban sustainability assessment. Finally, Sajjad et al [73] provide a quality computeraided blood analysis system to discover and count the white blood cells in blood samples.…”

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

“…Ju et al [71] design a framework to apply citizen-centered big data for governance intelligence in smart cities. Tan et al [72] develop an adaptive neuro-fuzzy inference system approach for urban sustainability assessment. Finally, Sajjad et al [73] provide a quality computer-aided blood analysis system to discover and count the white blood cells in blood samples.…”

“…As it is presented in Table 5, Luo et al [74] and Vázquez-Canteli et al [75] utilize deep learning to provide solutions in the energy sector for the smart cities. Where Luo et al [74] design a system for a short-term energy prediction Designing a framework to build a zeroemission neighborhood using responsive building envelope Energy Ju et al [71] Proposing a framework to apply citizencentered big data for governance intelligence in smart cities Evaluation and management of smart cities Tan et al [72] Developing an adaptive neuro-fuzzy inference system approach for urban sustainability assessment Evaluation and management of smart cities Sajjad et al [73] Providing a quality computer-aided blood analysis system to the discover and count the white blood cells in blood samples Health for a smart city. Vázquez-Canteli et al [75] develop an integrated simulation environment to manage energy intelligently.…”

Youth and forecasting of sustainable development pillars: An adaptive neuro-fuzzy inference system approach

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