Water resources carrying capacity evaluation of the Yellow River Basin based on EFAST weight algorithm

Zhang

et al. 2020

Water

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.

Section: Wrcca Results Validation In the Study Areamentioning

confidence: 90%

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

Zhang

et al. 2020

Water

“…The second category of approaches evaluates WRCC using non-integrated assessment methods. Techniques include the normal cloud model (Rabiei et al 2022;Zhang et al 2019), the system dynamics method (Wang et al 2021a;Yang and Wang 2022), and the ecological footprint method (Dai et al 2019). The normal cloud model has symmetric characteristics, and the evaluation cannot reflect the uncertainty in a standardized manner.…”

Section: Literature Reviewmentioning

confidence: 99%

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Sun

Zhou

Wang

2022

Environ Sci Pollut Res

The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.

“…In this method, the sensitivity of the model is divided into the sensitivity of the individual parameters acting independently and the sensitivity of the interactions between the parameters. Studies have shown that calculating weights using the EFAST algorithm is more scientific [17]. The EFAST algorithm is completed in SIMLAB software (https://ec.europa.…”

Section: Data Descriptionmentioning

confidence: 99%

Dynamic evaluation and regulation of water carrying state using a coupled iterative method

2022

The water carrying capacity and the water carrying state are two similar concepts, and there are no clear boundaries between them, so there exists confusion during the evaluation process. Additionally, current evaluation methods cannot meet the dynamic change requirements under different control measures. This study emphasizes the difference between the two concepts and points out that the core of the water carrying capacity is the determination of thresholds. In contrast, that of the water carrying state is a state evaluation. A coupled iterative model is proposed based on the extended Fourier amplitude sensitivity test algorithm and a modified state-space method. In order to evaluate and regulate the water carrying state dynamically, the iterative calculation is introduced into the evaluation of the water carrying state. During the iterative process, the water resources in the non-overloaded area are allocated to the surrounding adjacent overloaded areas until the water resource carrying state of the original over-loaded area reaches an acceptable level, and the total amount of water resources allocation during multiple iterations is given. In this study, we take Jilin Province as the research object. We hope that the iterative coupling model of the water carrying state proposed in this paper can be widely applied in the future.