Precondition Cloud and Maximum Entropy Principle Coupling Model-Based Approach for the Comprehensive Assessment of Drought Risk

Bai, Xia; Wang, Yimin; Jin, Juliang; Xiao-ming, QI; Wu, Chengguo

doi:10.3390/su10093236

Cited by 8 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) Comprehensive assessment of drought: in order to objectively elaborate the uncertainty of a drought risk system, Bai et al (2018) proposed the precondition cloud and maximum entropy principle coupling model (PCMEP), which can provide a new and reliable way to evaluate the comprehensive risk grade of drought; Wu et al (2019) put forward a precondition cloud algorithm and Copula coupling model, which can be further applied in drought hazard identification and assessment field; by using distributed hydrological modeling, Xu and Pan (2014) introduced a drought model to simulate the drought developing processes. Taking the severe drought events in the upper reaches of the Yangtze River in 2006 as an example, the application case study was carried out.…”

Section: Literature Review 21 Drought Risk Assessmentmentioning

confidence: 99%

Two-stage grey cloud clustering model for drought risk assessment

Luo

Zhang

2019

View full text Add to dashboard Cite

Purpose The purpose of this paper is to establish a two-stage grey cloud clustering model to assess the drought risk level of 18 prefecture-level cities in Henan Province. Design/methodology/approach The clustering process is divided into two stages. In the first stage, grey cloud clustering coefficient vectors are obtained by grey cloud clustering. In the second stage, with the help of the weight kernel clustering function, the general representation of the weight vector group of kernel clustering is given. And a new coefficient vector of kernel clustering that integrates the support factors of the adjacent components was obtained in this stage. The entropy resolution coefficient of grey cloud clustering coefficient vector is set as the demarcation line of the two stages, and a two-stage grey cloud clustering model, which combines grey and randomness, is proposed. Findings This paper demonstrates that 18 cities in Henan Province are divided into five categories, which are in accordance with five drought hazard levels. And the rationality and validity of this model is illustrated by comparing with other methods. Practical implications This paper provides a practical and effective new method for drought risk assessment and, then, provides theoretical support for the government and production departments to master drought information and formulate disaster prevention and mitigation measures. Originality/value The model in this paper not only solves the problem that the result and the rule of individual subjective judgment are always inconsistent owing to not fully considering the randomness of the possibility function, but also solves the problem that it’s difficult to ascertain the attribution of decision objects, when several components of grey clustering coefficient vector tend to be balanced. It provides a new idea for the development of the grey clustering model. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

show abstract

Section: Literature Review 21 Drought Risk Assessmentmentioning

confidence: 99%

Two-stage grey cloud clustering model for drought risk assessment

Luo

Zhang

2019

View full text Add to dashboard Cite

show abstract

“…The cloud model can comprehensively consider randomness and fuzziness; realize the mutual transformation between numerical features and linguistic values; and simulate randomness, fuzziness, and correlation between the two by using a cloud generator [21,22]. Based on uncertainty analysis, the cloud generator can describe the stochasticity of the disaster-causing factors, the disaster-bearing environment, the disasterbearing body, the regional disaster response capacity, and the fuzziness of the information expression, and the cloud model has been widely used in the evaluation of indicators in many fields [23][24][25][26][27], including regional-scale water resource carrying capacity assessment, flood resource utilization risk assessment, drought assessment, and fire risk assessment. Applying the cloud model to the evaluation of flood disaster risk levels can enable the uncertainty mapping of the quantitative values of each assessment indicator to a qualitative evaluation level.…”

Section: Related Workmentioning

confidence: 99%

Flood Risk Assessment Based on a Cloud Model in Sichuan Province, China

Liu,

Wang,

et al. 2023

Sustainability

View full text Add to dashboard Cite

Floods are serious threats to the safety of people’s lives and property. This paper systematically introduces the basic theories and methods of flood risk assessment, takes Sichuan Province as the study area, and establishes a flood risk assessment index system with 14 indicators in four aspects—disaster-causing factors, disaster-forming environment, disaster-bearing body, and regional disaster resilience capacity—from the causes of disaster losses and flood formation mechanisms. Furthermore, this paper constructs a flood disaster risk assessment model for Sichuan Province based on a cloud model, entropy value, and GIS technology. The model is validated by taking the July–August 2023 flood disaster as an example, and the results show that the distribution of the disaster was consistent with the flood risk assessment results of this paper, which verifies that the selected indicators are appropriate and the model is accurate and valid.

show abstract

“…Previous studies have used data over the past few years, as research databases, to determine the weight of the indicators [29,30]. However, the database formed by retrieving such past data is not comprehensive, lacks relevance, and can only explain the problem of the norm.…”

Section: Evaluation Processmentioning

confidence: 99%

Fuzzy Risk Assessment of Tunnels’ Undercrossing Existing Structures

Wang

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Numerical calculation is usually employed to analyse the safety risks of tunnel construction under existing structures. However, due to the fuzziness and randomness of the construction environment, the simulation results fluctuate significantly. Given the fuzziness and randomness encountered during the safety risk assessment of tunnels undercrossing existing structures, this study analyses and summarises the previous experiences and selects a relevant algorithm of the cloud model to solve the uncertainty problem during the assessment. The study proposes three forms of cloud models to evaluate the risks of tunnels undercrossing existing structures and performs a comparative analysis between them. Finally, the effectiveness and feasibility of the cloud model calculation method are verified through a case study. The research shows that (1) the accuracy of the one-dimensional and two-dimensional cloud models was high, and the difference was small, indicating that the cloud model method used to determine the risk level of tunnels underpassing existing structures was reasonably effective, (2) since the soft ‘and’ two dimensions operated computation, the distribution probability was dispersed, making the soft ‘and’ computation more affected by the index, resulting in lower accuracy than the one-dimensional and two-dimensional cloud models, and (3) compared with numerical calculation, the cloud model calculation was more convenient, reflecting the relationship between the indices and the relationship between the indices and the complex whole. The evaluation results used the comprehensive evaluation probability to determine the risk level so that the evaluation results were reliable.

show abstract

Precondition Cloud and Maximum Entropy Principle Coupling Model-Based Approach for the Comprehensive Assessment of Drought Risk

Cited by 8 publications

References 19 publications

Two-stage grey cloud clustering model for drought risk assessment

Two-stage grey cloud clustering model for drought risk assessment

Flood Risk Assessment Based on a Cloud Model in Sichuan Province, China

Fuzzy Risk Assessment of Tunnels’ Undercrossing Existing Structures

Contact Info

Product

Resources

About