Study on Water Resources Carrying Capacity in Hefei City

Gao, Yang; Sheng, Zhang; Xu, Guo Wei; Su, Hai Min; Zhang, Yong

doi:10.4028/www.scientific.net/amr.610-613.2701

Cited by 9 publications

(14 citation statements)

References 2 publications

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“…Many factors influence the WRCC. Most studies only consider the impact of the amount of water resources, social economy, and ecological environment on WRCC [24][25][26] but ignore the impact of water quality on regional WRCC. This tends to make the calculation of the WRCC too large and fails to reflect the real situation in the study area.…”

Section: Evaluation Of Index Selectionmentioning

confidence: 99%

Water Resource Carrying Capacity Based on Water Demand Prediction in Chang-Ji Economic Circle

Wang

Xiao

et al. 2020

Water

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In view of the large spatial difference in water resources, the water shortage and deterioration of water quality in the Chang-Ji Economic Circle located in northeast China, the water resource carrying capacity (WRCC) from the perspective of time and space is evaluated. We combine the gray correlation analysis and multiple linear regression models to quantitatively predict water supply and demand in different planning years, which provide the basis for quantitative analysis of the WRCC. The selection of research indicators also considers the interaction of social economy, water resources, and water environment. Combined with the fuzzy comprehensive evaluation method, the gray correlation analysis and multiple linear regression models to quantitatively and qualitatively evaluate the WRCC under different social development plans. The developmental trends were obtained from 2017 to 2030 using four plans designed for distinct purposes. It can be seen that the utilization of water resource is unreasonable now and maintains a poor level under a business-as-usual Plan I. Plan II and Plan III show that resource-based water shortage is the most critical issue in this region, and poor water quality cannot be ignored either. Compared with Plan I, the average index of WRCC in Plan IV increased by 51.8% and over 84% of the regions maintain a good level. Strengthening sewage treatment and properly using transit water resources are more conducive to the rapid development of Chang-Ji Economic Circle.

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Section: Evaluation Of Index Selectionmentioning

confidence: 99%

Water Resource Carrying Capacity Based on Water Demand Prediction in Chang-Ji Economic Circle

Wang

Xiao

et al. 2020

Water

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“…Lu et al Used AHP to study the water environment carrying capacity of Huai'an City from 2005 to 2014 20 , Zhang et al Used a coupled model to study the water environment carrying capacity of Guizhou Province 21 , and Yang et al Predicted the development trend of WRCC in Xi'an City through SD model 22 . Gao et al Analyzed the dynamic changes and influencing factors of WRCC in Hefei city 23 . Others have used the ecological footprint method to quantitatively assess water security 24 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of regional water resources carrying capacity in China based on variable weight model and grey-markov model: a case study of Anhui province

Yan,

Jiao,

Yongshi

2023

Sci Rep

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Water security is not only an ecological environmental issue but also a bearing on national security and development. The study of water resources carrying capacity is the basis for future socioeconomic development and is the driving force for social progress. Therefore, it is important to investigate the influence factors of regional and national water resources carrying capacity (WRCC) and predict the future trend development. In view of the regional water resources data of the past 10 years in Anhui province, China, the Driving force Pressure State Impact Response Management (DPSIRM) model framework is constructed and the entropy weight method and variable weight theory can be used to make a comprehensive evaluation of the WRCC. Based on the comprehensive evaluation value, a modified Grey-Markov combination forecast can be introduced to predict the local WRCC in the coming years. The study on account of the Anhui Statistical Yearbook, the Water Resources Bulletin, and the water resources data of the Forestry Bureau for the past 10 years shows that the WRCC of Anhui Province is weak from 2010 to 2013 and gradually strengthens from 2014 to 2019; the WRCC of Anhui Province is mainly correlated with the impact subsystem, the management subsystem, and the state subsystem. The combined projections reflect that the future WRCC of Anhui Province is in good condition. It is recommended that the Anhui provincial government should strengthen water security and management, improve water resources utilization techniques, and construct complete and effective management tools and measures to fundamentally safeguard the province's water resources security and improve the WRCC.

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“…One is the largest economic and social development scale that water resources can carry, and the other is the maximum carrying capacity of water resources to the existing economic and social development scale. The carrying scale is usually evaluated by system dynamics, conventional trend, and multi-objective analysis methods [11,12], while the carrying capacity is usually evaluated by principal component analysis (PCA), projection pursuit evaluation, analytical hierarchy process (AHP), fuzzy comprehensive evaluation (FCE), neural network, and machine learning methods [13][14][15]. The establishment of an evaluation indicator system is the first step for WRCCA.…”

Section: Introductionmentioning

confidence: 99%

“…One is to divide the water resources system into different levels of subsystems which consist of different indicator elements, and these indicators are interconnected or independent from each other by tapping their meanings based on the expert advice [16]. The other is to select indicators according to the fixed-mode of Driving Force-Pressure-State-Impact-Response-Management (DPSIRM) [15,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach for Water Resources Carrying Capacity Assessment by Integrating Fuzzy Comprehensive Evaluation and Analytical Hierarchy Process Methods with the Cloud Model

Ren

Zhang

Ren

et al. 2020

Water

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The water resources carrying capacity (WRCC) shows remarkable fuzziness and randomness, which causes the uncertainty and instability of the WRCC assessment (WRCCA). In order to solve these problems, we proposed a novel hybrid approach for WRCCA, in which the fuzzy comprehensive evaluation (FCE) and analytical hierarchy process (AHP) methods were integrated with the cloud model (CM). Firstly, an evaluation indicator system of WRCC was constructed. Secondly, the AHP and FCE methods were subsequently improved with the CM. The CM was used to scale the relative importance and aggregate the judgment matrices, where the weights of the clouds were obtained. These integrations of AHP and CM greatly reduced the randomness in the weight calculation; the CM was used to describe the comment sets, calculate the membership degree matrices and determine the assignment clouds, the evaluation sets and the WRCCA index clouds were obtained. These integrations of FCE and CM effectively blurred the boundary fuzziness and gave more intuitive results. Finally, the hybrid FCE-AHP-CM approach was applied to a case study. It was concluded that the novel approach has particular advantages in dealing with the fuzziness and randomness comprehensively, and therefore could assess the WRCC and enhance the robustness and intuition of WRCCA results.

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Study on Water Resources Carrying Capacity in Hefei City

Cited by 9 publications

References 2 publications

Water Resource Carrying Capacity Based on Water Demand Prediction in Chang-Ji Economic Circle

Water Resource Carrying Capacity Based on Water Demand Prediction in Chang-Ji Economic Circle

Evaluation of regional water resources carrying capacity in China based on variable weight model and grey-markov model: a case study of Anhui province

A Novel Hybrid Approach for Water Resources Carrying Capacity Assessment by Integrating Fuzzy Comprehensive Evaluation and Analytical Hierarchy Process Methods with the Cloud Model

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