A Secure IoT-Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision-making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user-specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance-based approach (DBA) aided by Fuzzy Set Theory.

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Land subsidence due to groundwater pumping: hazard probability assessment through the combination of Bayesian model and fuzzy set theory

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-823-2021 ◽

2021 ◽

Vol 21 (2) ◽

pp. 823-835

Author(s):

Huijun Li ◽

Lin Zhu ◽

Gaoxuan Guo ◽

Yan Zhang ◽

Zhenxue Dai ◽

...

Keyword(s):

Set Theory ◽

Land Subsidence ◽

Fuzzy Set ◽

Groundwater Level ◽

Fuzzy Set Theory ◽

Bayesian Model ◽

Large Scale ◽

Alluvial Fan ◽

Prevention Measures ◽

Hazard Assessments

Abstract. Land subsidence caused by groundwater overpumping threatens the sustainable development in Beijing. Hazard assessments of land subsidence can provide early warning information to improve prevention measures. However, uncertainty and fuzziness are the major issues during hazard assessments of land subsidence. We propose a method that integrates fuzzy set theory and weighted Bayesian model (FWBM) to evaluate the hazard probability of land subsidence measured by Interferometric Synthetic Aperture Radar (InSAR) technology. The model is structured as a directed acyclic graph. The hazard probability distribution of each factor triggering land subsidence is determined using Bayes' theorem. Fuzzification of the factor significance reduces the ambiguity of the relationship between the factors and subsidence. The probability of land subsidence hazard under multiple factors is then calculated with the FWBM. The subsidence time series obtained by InSAR is used to infer the updated posterior probability. The upper and middle parts of the Chaobai River alluvial fan are taken as a case-study site, which locates the first large-scale emergency groundwater resource region in the Beijing plain. The results show that rates of groundwater level decrease more than 1 m yr−1 in the confined and unconfined aquifers, with cumulative thicknesses of the compressible sediments between 160 and 170 m and Quaternary thicknesses between 400 and 500 m, yielding maximum hazard probabilities of 0.65, 0.68, 0.32, and 0.35, respectively. The overall hazard probability of land subsidence in the study area decreased from 51.3 % to 28.3 % between 2003 and 2017 due to lower rates of groundwater level decrease. This study provides useful insights for decision makers to select different approaches for land subsidence prevention.

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Land Subsidence due to groundwater pumping: Hazard Probability Assessment through the Combination of Bayesian Model and Fuzzy Set Theory

10.5194/nhess-2020-309 ◽

2020 ◽

Author(s):

Huijun Li ◽

Lin Zhu ◽

Gaoxuan Guo ◽

Yan Zhang ◽

Zhenxue Dai ◽

...

Keyword(s):

Set Theory ◽

Land Subsidence ◽

Fuzzy Set ◽

Groundwater Level ◽

Fuzzy Set Theory ◽

Bayesian Model ◽

Large Scale ◽

Alluvial Fan ◽

Prevention Measures ◽

Hazard Assessments

Abstract. Land subsidence caused by groundwater over-pumping threatens the sustainable development in Beijing. Hazard assessments of land subsidence can provide early warning information to improve prevention measures. However, uncertainty and fuzziness are the major issues during hazard assessments of land subsidence. We propose a method that integrates fuzzy set theory and weighted Bayesian model (FWBM) to evaluate the hazard probability of land subsidence measured by Interferometric Synthetic Aperture Radar (InSAR) technology. The model is structured as a directed acyclic graph. The hazard probability distribution of each factor triggering land subsidence is determined using Bayes’ theorem. Fuzzification of the factor significance reduces the ambiguity of the relationship between the factors and subsidence. The probability of land subsidence hazard under multiple factors is then calculated with the FWBM. The subsidence time-series obtained by InSAR is used to infer the updated posterior probability. The upper and middle parts of the Chaobai River alluvial fan is taken as a case-study site, which locates the first large-scale Emergency Groundwater Resource Region in Beijing plain. The results show that rates of groundwater level decrease larger than 1 m/y in the confined and unconfined aquifers, compressible layer thicknesses between 160 and 170 m, and Quaternary thicknesses between 400 and 500 m yield maximum hazard probabilities of 0.65, 0.68, 0.32, and 0.35, respectively. The overall hazard probability of land subsidence in the study area decreased from 51.3 % to 28.3 % between 2003 and 2017 due to lower rates of groundwater level decrease. This study provides useful insights for decision-makers to select different approaches for land subsidence prevention.

Download Full-text