Literature DB >> 32319185

Research on constructing sponge city indicator and decision evaluation model with fuzzy multiple criteria method.

Hsueh-Sheng Chang1, Qingmu Su1.   

Abstract

Cities face acute shock like hurricanes and floods, and chronic stresses such as droughts, water shortages, urban floods, and urban using water. How cities can create a development model in which the water and environment can coexist to alleviate the problem with urban water has become a common problem faced by countries or cities. Sponge city construction becomes a possible option to meet these challenges. According to the dimensions of pre-assessment, this article constructs the sponge city assessment indicator which is divided into three aspects: water ecosystem, socioeconomic system, and institutional and mechanism system; then, the degree of importance and degree of satisfaction are applied as the evaluation framework for the fuzzy multiple criteria to perform the comparison between the government officials and the public regarding the evaluation to analyze group differences. The results are that the difference in the evaluation dimensions between the officials and the public is between 0.8 and 1, but there exists difference in the degree of importance of water management and water use, and in the degree of satisfaction with water development, water efficiency, and mechanism. This result is helpful to find the problems and provide a decision basis for the further exploration. PRACTITIONER POINTS: Constructed indicators for sponge city construction, which can be used to evaluate stormwater management. Using fuzzy multiple criteria to compare officials and public can be used as a basis for decision-making in water management. Constructed three indicators of sponge city: water ecosystem, socioeconomic system, and institutional and mechanism system.
© 2020 Water Environment Federation.

Entities:  

Keywords:  decision evaluation; fuzzy multiple criteria; indicator system; sponge city; variation measure method

Mesh:

Substances:

Year:  2020        PMID: 32319185     DOI: 10.1002/wer.1344

Source DB:  PubMed          Journal:  Water Environ Res        ISSN: 1061-4303            Impact factor:   1.946


  1 in total

1.  The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence.

Authors:  Shangen Wang; Wei Zhang
Journal:  Comput Intell Neurosci       Date:  2022-08-03
  1 in total

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