Cloud model driven assessment of interregional water ecological carrying capacity and analysis of its spatial-temporal collaborative relation

Yang, Lingzhi; Chen, Yizhong; Lu, Hongwei; Qiao, Youfeng; He, Peng; He, Pengming; Zhao, Yunshuo

doi:10.1016/j.jclepro.2022.135562

Cited by 8 publications

(3 citation statements)

References 58 publications

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“…In order to adapt to the fuzzy attribute of multi-criteria problems, this paper uses the Monte Carlo Simulation Method to select the weight value within the tested weight boundary, so as to obtain a reasonable and fuzzy final weight value. Considering the extensibility and reliability of the Cloud Model in the fuzzy decision-making field [29][30][31], we construct a normal comprehensive cloud model to expand the coverage at the grade boundary, thus obtaining the final risk classification.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of debris flow disaster based on the cloud model—Probability fusion method

Qiang

et al. 2023

PLoS ONE

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This paper proposes a new debris flow risk assessment method based on the Monte Carlo Simulation and an Improved Cloud Model. The new method tests the consistency of coupling weights according to the characteristics of the Cloud Model firstly, so as to determine the weight boundary of each evaluation index. Considering the uncertain characteristics of weights, the Monte Carlo Simulation is used to converge the weights in a minimal fuzzy interval, then the final weight value of each evaluation index is obtained. Finally, a hierarchical comprehensive cloud is established by the Improving Cloud Model, which is used to input the comprehensive expectation composed of weights to obtain the risk level of debris flow. Through statistical analysis, this paper selects Debris flow scale (X1), Basin area (X2), Drainage density (X3), Basin relative relief (X4), Main channel length (X5), Maximum rainfall (X6) as evaluation indexes. A total of 20 debris flow gullies were selected as study cases (8 debris flow gullies as model test, 12 debris flow gullies in reservoir area as example study). The comparison of the final evaluation results with those of other methods shows that the method proposed in this paper is a more reliable evaluation method for debris flow prevention and control.

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Section: Introductionmentioning

confidence: 99%

Risk assessment of debris flow disaster based on the cloud model—Probability fusion method

Qiang

et al. 2023

PLoS ONE

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“…The above methods have specific advantages and disadvantages, but the data in the evaluation of water network infrastructure construction [25] effects are not fixed values but fluctuate within a certain range, which is uncertain and also requires the combination of qualitative concepts and quantitative indicators. The cloud model [26] proposed by Li Deyi based on the traditional fuzzy set theory and conceptual statistics theory can effectively solve the problems of uncertainty and ambiguity [27], and it is widely used in the evaluation of renewable energy [28], the health status of water cycle evaluation [29], comprehensive evaluation of water resources carrying capacity [21], ecological environment vulnerability evaluation [30], and other fields. In view of the above problems, this paper uses the game-weighting matter-element cloud model to balance the subjectivity and objectivity of indicator weights and solve the uncertainty and ambiguity of indicators in the evaluation process.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Water Network Construction Effect Based on Game-Weighting Matter-Element Cloud Model

Zhang

Huang

et al. 2023

Water

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Water network construction is one of the important ways to solve complex water problems at present. It is crucial for the optimal allocation of water resources, flood control, disaster reduction, protection of water ecology, water security, and sustainable urban development. Accordingly, this study formulates an index system for assessing the efficacy of water network construction based on the Driving Force–Pressure–State–Influence–Response (DPSIR) model, taking into account the four dimensions of optimal allocation of water resources, flood control and disaster reduction in river basins, protection of water ecosystems, and intelligent water network management. The proposed index system comprises four key aspects, which are utilized to evaluate the effectiveness of water network construction efforts. Then, the game-weighting method and the matter-element extension method improved by the cloud theory established an evaluation model to evaluate and compare the water network construction effects of the two cities in Henan Province. Finally, the GM (1,1) model was used to evaluate the water network construction effects, and future trends were predicted. The results show the following: (1) On the whole, the effect of water network construction in the two cities is constantly improving; (2) There has been a significant improvement in the intelligent management of water networks. The main reason for this result is that the “Internet +” has promoted the intelligent construction of water networks; (3) The water ecological environment, flood control, and drainage capabilities continued to improve, which has largely guaranteed the basic security bottom line of urban development space; (4) The advancement and utilization of water resources has undergone gradual improvements over time, with key impact metrics centered on water supply safety factors and the development and utilization of water resources. As water supply sources continue to diversify, it is expected that the aforementioned situation will be ameliorated in the future; (5) The predicted value shows that the water network construction of the two cities can basically meet the planned value of each index when the water network construction reaches the planning level. This paper provides help to promote the sustainable use of water resources and ensure the sustainable development of cities.

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“…Monte Carlo simulation, as a classic random number simulation method, has been extended to various fields such as medicine (Santos et al, 2022), water conservancy (Vihola et al, 2020), and science and technology materials (Wang et al, 2020). Considering the scalability and reliability of cloud models in fuzzy decision-making fields (Mao et al, 2018;Mao et al, 2022;Yang et al, 2023), we developed a "cloud model-Monte Carlo" coupling model to calculate slope failure probability and perform risk assessment.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of slopes based on cloud model-Monte Carlo coupling

Chen

Sun

et al. 2023

Front. Earth Sci.

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Risk analysis of dam slopes is crucial for ensuring the safety and stability of hydraulic engineering. To improve the accuracy and reliability of risk analysis, we adopt the cloud theory approach and conduct a study on the distribution types of soil shear strength indicators based on indoor geotechnical tests. We propose a “cloud model-Monte Carlo” coupling model that uses the cloud model to describe the uncertainty of risk factors and determine the probability distribution types of shear strength parameters, while the Monte Carlo method is used to simulate random variables in the model. The effectiveness of the proposed model is validated through a risk analysis of a slope of an earth-rock dam, with results showing significantly greater accuracy and reliability compared to traditional methods. The calculation results show that the risk probability corresponding to the design flood level of the dam is 9.01×10-6, exceeding its allowable risk standard of 0.5×10-6, hence the need for reinforcement treatment. The proposed model can accurately evaluate the risk of dams and provide the scientific basis for decision-making in dam safety management.

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Cloud model driven assessment of interregional water ecological carrying capacity and analysis of its spatial-temporal collaborative relation

Cited by 8 publications

References 58 publications

Risk assessment of debris flow disaster based on the cloud model—Probability fusion method

Risk assessment of debris flow disaster based on the cloud model—Probability fusion method

Evaluation of Water Network Construction Effect Based on Game-Weighting Matter-Element Cloud Model

Stability analysis of slopes based on cloud model-Monte Carlo coupling

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