2020
DOI: 10.3390/w13010016
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Water Resource Carrying Capacity Based on Water Demand Prediction in Chang-Ji Economic Circle

Abstract: 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 resear… Show more

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Cited by 18 publications
(8 citation statements)
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“…The coupling degree model was corrected based on the original formula. In this study, it tried to make C within [0,1] to increase discrimination, and its equation is as follows (Wang et al, 2021):…”
Section: Coupling Degree Modelmentioning
confidence: 99%
“…The coupling degree model was corrected based on the original formula. In this study, it tried to make C within [0,1] to increase discrimination, and its equation is as follows (Wang et al, 2021):…”
Section: Coupling Degree Modelmentioning
confidence: 99%
“…Therefore, we need to calculate the correlation between different indicators, from which we can select the appropriate characteristics for water quality assessment. Commonly used correlation analysis methods include Pearson correlation coefficient analysis ( Zhang et al., 2022a ), Granger causality analysis ( Tang et al., 2020 ), and grey relational analysis ( Wang et al., 2020 ). Most indicators affecting water quality are grey and localized.…”
Section: Introductionmentioning
confidence: 99%
“…Unlike the long-term early warning (three to five years) that is helpful for long-term planning, the short-term early warning (one to two years) is mainly used for assessing and predicting the water environmental situation in watershed management of the next year. Nevertheless, existing quantitative methods and models for the short-term early warning of the water environmental system are based mainly on the future trend prediction of single and independent early warning indicators and have rarely focused on the integrated change trends of the early warning indicators; these methods and models mainly include the variable fuzzy pattern recognition (VFPR) (Wang and Xu, 2015), autoregressive moving average model (ARMA) , multiple linear regression model (MLRM) (Wang et al, 2021), gray forecast model (GM) (Lu and Tang, 2019), and artificial neural networks (ANNs) (Maier et al, 2010;Yu et al, 2020;Cao et al, 2021;Chen et al, 2022). In actuality, however, the change in the socioeconomic system is cyclical and is affected by the quantity of flow (e.g., monthly, quarterly, and annual changes).…”
Section: Introductionmentioning
confidence: 99%