Regional Water Circulation Health Evaluation System Based on Artificial Neural Network

Lv, Chunming; Zhang, Hui; Wang, Xiao; Liu, Junliang

doi:10.18280/ijdne.150211

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…is paper chooses three urban agglomerations in China for comparative analysis, namely, Guangdong-Hong Kong-Macao (GHKM) Greater Bay Area, Beijing-Tianjin-Hebei (BTH) Urban Agglomeration, and Yangtze River Delta (YRD) Urban Agglomeration [18,19]. ese regions were selected for the following advantages: the three urban agglomerations boast the earliest integrated development, most advanced economy, and most mature collaboration networks in China.…”

Section: Object Selection and Data Sourcesmentioning

confidence: 99%

Evaluation of the Production-Education Integration Performance of the High-Tech Industry: An Empirical Comparison between Three Urban Agglomerations in China

Zhang

et al. 2021

Discrete Dynamics in Nature and Society

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Deepening production-education integration (PEI) is a major strategy of China to reinforce education reform and promote industry transformation. Different from the traditional research perspective of colleges on the supple side, this paper chooses to evaluate the PEI performance of the high-tech industry from the angle of industry on the demand side. Firstly, the action mechanisms of the education system and industry system in PEI were expounded, and the design framework of the evaluation index system (EIS) was derived from the interactions between two elements (technologies and talents) and two market relationships (supply and demand), forming a two-dimensional four-quadrant EIS. On this basis, the correlation-entropy composite matter-element (CECME) model and coupling coordination degree (CCD) model were introduced to empirically compare the PEI performance of the high-tech industry between Guangdong-Hong Kong-Macao (GHKM) Greater Bay Area, Beijing-Tianjin-Hebei (BTH) Urban Agglomeration, and Yangtze River Delta (YRD) Urban Agglomeration. The main conclusions are as follows: (1) the GHKM Greater Bay Area is far better in education and industry development than YRD Urban Agglomeration and BTH Urban Agglomeration; (2) the PEI performance of the high-tech industry in GHKM Greater Bay Area was evaluated as high level with industry in lead, that in YRD Urban Agglomeration as moderate level with education in lead, and that in BTH Urban Agglomeration high-tech industry PEI as moderate level with industry in lag. Finally, several countermeasures were proposed for the urban agglomerations to further enhance the PEI performance of the high-tech industry: GHKM Greater Bay Area should improve the weak link of education development; YRD Urban Agglomeration should give full play to its advantages in higher education; BTH Urban Agglomeration should focus on high-quality industry development. From the practical level, this paper objectively evaluates the actual performance of PEI and provides a reference for scientific policymaking of PEI development.

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Section: Object Selection and Data Sourcesmentioning

confidence: 99%

Evaluation of the Production-Education Integration Performance of the High-Tech Industry: An Empirical Comparison between Three Urban Agglomerations in China

Zhang

et al. 2021

Discrete Dynamics in Nature and Society

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“…Before empirically analyzing the degree of collaboration of higher education, it is necessary to construct an EIS with indices that scientifically reflect the correlations and features of higher education and its subsystems. In general, the EIS for higher education should have functions like description, interpretation, evaluation, monitoring and prediction [22][23][24].…”

Section: Eis Constructionmentioning

confidence: 99%

Measurement and Evaluation of Collaborative Development Level of Higher Education

Liu

Wang

Kang

2021

Int. J. Emerg. Technol. Learn.

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With the aid of grey correlation theory and technique for order of preference by similarity to ideal solution (TOPSIS), this paper sets up a multi-dimensional evaluation index system (EIS) for the comprehensive development level (CDL) of China’s higher education, and quantifies the state of collaborative development in 2005-2017 with an improved distance collaboration model. The results show that: Being the premises and bases of higher education, the supply subsystem develops much slower than the other subsystems (i.e. participation, output, and environment), and clearly pulls the development of the other subsystems. From 2012 onwards, the subsystems conformed to basically the same trend for the degree of collaboration: the degree of collaboration tended to be stable. There was even a slight dent in overall degree of collaboration between 2012 and 2016. Hence, the overall degree of collaborative development was far slower than the overall degree of development. After more than a decade of development, the degree of development, degree of collaboration, and degree of collaborative development reached basically the same level in 2017. The empirical results shed new lights on the focal points of higher education development in China.

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“…The quality of drainage facilities was directly related to the development, landscape and sanitation of the city [7]. Lv Chungming first established the regional water cycle health evaluation index system, and on this basis built the comprehensive evaluation BPNN model of water cycle health [8]. Through the use of scientific and technological means, Tekin Senem analyzed the factors affecting WP.…”

Section: Prefacementioning

confidence: 99%

Evaluation of Water Pollution Prevention Planning Based on Urban and Rural Integration Based on BP Neural Network

2021

WPPCP

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Regional Water Circulation Health Evaluation System Based on Artificial Neural Network

Cited by 3 publications

References 21 publications

Evaluation of the Production-Education Integration Performance of the High-Tech Industry: An Empirical Comparison between Three Urban Agglomerations in China

Evaluation of the Production-Education Integration Performance of the High-Tech Industry: An Empirical Comparison between Three Urban Agglomerations in China

Measurement and Evaluation of Collaborative Development Level of Higher Education

Evaluation of Water Pollution Prevention Planning Based on Urban and Rural Integration Based on BP Neural Network

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