A new Bayesian network model for risk assessment based on cloud model, interval type-2 fuzzy sets and improved D-S evidence theory

Xu, Jintao; Ding, Rui; Li, Muye; Dai, Tao; Zheng, Mengyan; Tao, Yu; Sui, Yang

doi:10.1016/j.ins.2022.11.011

Cited by 21 publications

(2 citation statements)

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“…Definition 9 [64]. The modified information fusion rule is utilized to fuse the conflicting information from experts' knowledge using Equations ( 8)- (10), and the D-S ET is modified.…”

Section: Definition 7 the Cm-it2 Fs-based Membership Degree Set In De...mentioning

confidence: 99%

A New Fuzzy Bayesian Inference Approach for Risk Assessments

Xu,

Sui,

et al. 2024

Symmetry

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Bayesian network (BN) inference is an important statistical tool with additive symmetry. However, BN inference cannot deal with the uncertain, fuzzy, random, and conflicting information from experts’ knowledge in the process of conducting a risk assessment. To tackle this issue, a new fuzzy BN inference approach for risk assessments was proposed based on cloud model (CM), interval type-2 fuzzy set (IT2 FS), interval type-2 fuzzy logic system (IT2 FLS), modified Dempster–Shafer (D-S) evidence theory (ET), and Latin hypercube sampling (LHS) methods along the following lines. Firstly, CM was integrated into IT2 FS, and CM-based IT2 FS (CM-IT2 FS) was defined in the IT2 FLS. Secondly, modified D-S ET was utilized to determine the CM-IT2 FS-based a priori probabilities, and the CM-IT2 FS-based BN model was established. Thirdly, the CM-IT2 FS-based a priori probabilities were reduced to the CM-IT1 FS-based ones using a type reducer in the IT2 FLS, LHS was applied to propose a new fuzzy BN inference algorithm, and then, the new algorithm was used in a typical case to perform the fuzzy BN positive inference for risk prediction and the fuzzy BN reverse inference for risk sensitivity analysis. Finally, the BN inference results were analyzed using the proposed algorithm and the two common BN inference algorithms, and the effectiveness of the proposed approach was validated. It can be concluded that the proposed approach was both accurate and promising.

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“…Definition 9 [64]. The modified information fusion rule is utilized to fuse the conflicting information from experts' knowledge using Equations ( 8)- (10), and the D-S ET is modified.…”

Section: Definition 7 the Cm-it2 Fs-based Membership Degree Set In De...mentioning

confidence: 99%

A New Fuzzy Bayesian Inference Approach for Risk Assessments

Xu,

Sui,

et al. 2024

Symmetry

View full text Add to dashboard Cite

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“…The subjective nature of terms like "high" makes fuzzy theory a common choice [16,26,27], but this theory struggles to capture the varying and vague membership degrees assigned by different individuals. The cloud model [28], as an advancement in membership function, exhibits potential in capturing complex variability in uncertainty-based evaluation [29][30][31][32][33], knowledge representation [34,35] and data mining [36]. The cloud model assumes that the membership degree between a real number and a concept is not fixed, but a random number [37], which aligns more closely with real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties

Zhang,

Tang,

Zhou

et al. 2023

Agronomy

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In arid agriculture, the effective allocation of scarce water resources and the assessment of irrigation shortage risks are critical water management practices. However, these practices are faced with inherent and unignorable uncertainties affecting multiple variables. This study aims to model the typical uncertainties in these practices and understand how they impact the allocation of scarce water resources. We advocate for a nuanced consideration of variable characteristics and data availability, variation, and distribution when choosing uncertainty representation methods. We proposed a comprehensive framework that integrates the cloud model to delineate scenarios marked by subjective vagueness, such as “high” or “low” prices. Simultaneously, the stochastic method was used for modeling meteorological and hydrological variables, notably precipitation and crop evapotranspiration. Additionally, to navigate subjectivity and imprecise judgment in standards classification, this framework contains a cloud-model-based assessment method tailored for evaluating irrigation shortage risks. The proposed framework was applied to a real-world agricultural water management problem in Liangzhou County, northwest China. The results underscored the efficacy of the cloud model in representing subjective vagueness, both in the optimization process and the subsequent assessment. Notably, our findings revealed that price predominantly influences net benefits, and that precipitation and crop evapotranspiration emerge as decisive factors in determining optimal irrigation schemes. Moreover, the identification of high water storage risks for maize in the Yongchang and Jinyang districts serves as a reminder for local water managers of the need to prioritize these areas. By adeptly modeling multiple uncertainties, our framework equips water managers with tools to discern sensitive variables. We suggest that enhanced precipitation and evapotranspiration forecasts could be a promising way to narrow the uncertainties.

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