A Probabilistic Framework to Evaluate Seismic Resilience of Hospital Buildings Using Bayesian Networks

Liu, Jin; Zhai, Changhai; Peng, Yu

doi:10.1016/j.ress.2022.108644

Cited by 37 publications

(2 citation statements)

References 63 publications

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“…Zhou et al (2021) predicted the credit risk of borrowers using four ensemble models with gradient boosting. Liu, Zhai, and Yu (2022) proposed two tree‐based augmented gradient boosting decision tree (GBDT) for credit scoring. Wang (2022) combined the evaluation of the synthetic minority oversampling technique and fuzzy multicore C‐means with integrated models and used particle swarm optimization to enhance the evaluation performance of the model.…”

Section: Review Of the Literaturementioning

confidence: 99%

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

Liu,

Zhang,

Xiong

2024

Journal of Forecasting

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The predictive and interpretable power of models is crucial for financial risk management. The purpose of this study was to perform credit risk prediction in a structured causal network with four stages—data processing, structural learning, parameter learning, and interpretation of inferences—and use six real credit datasets to conduct empirical research on the proposed model. Compared with traditional machine learning algorithms, we comprehensively explain the results of credit default through forward and reverse reasoning. We also compared our model with the post hoc interpretation models local interpretable model‐agnostic explanations (LIME) and shapley additive explanations (SHAP) to verify the interpretability of Bayesian networks. The experimental results show that the prediction performance of Bayesian networks is superior to traditional machine learning models and similar to the performance of ensemble models. Furthermore, Bayesian networks offer valuable insights into the interplay of features by considering their causal relationships and enable an assessment of how individual features influence the prediction outcome. In this study, what‐if analysis was performed to assess credit default probabilities under various conditions. This analysis provides decision‐makers with the necessary tools to make informed judgments about the risk profile of borrowers. Consequently, we consider Bayesian networks as a viable tool for credit risk prediction models in terms of prediction performance and interpretability.

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Section: Review Of the Literaturementioning

confidence: 99%

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

Liu,

Zhang,

Xiong

2024

Journal of Forecasting

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“…The analysis of communities' resilience has been widely investigated in recent years [1][2][3][4][5][6]. Resilience, despite including the proper contents of a risk mitigation analysis, extends its investigation to the capacity of a system to recover its properties and its functionality after a catastrophic event.…”

Section: Introductionmentioning

confidence: 99%

Seismic Resilience of Emergency Departments: A Case Study

Pianigiani,

Viti,

Tanganelli

2024

Modelling

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In this work, the seismic resilience of the Emergency Department of a hospital complex located in Tuscany (Italy), including its nonstructural components and organizational features, has been quantified. Special attention has been paid to the ceilings, whose potential damage stood out in past earthquakes. A comprehensive metamodel has been set, which can relate all the considered parameters to the assumed response quantity, i.e., the waiting time of the yellow-code patients arriving at the Emergency Department in the hours immediately after the seismic event. The seismic resilience of the Emergency Department has been measured for potential earthquakes compatible with the seismic hazard of the area.

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Seismic disaster resilience by improving infrastructure strength in active seismotectonic zones of Sikkim Himalaya, India—An integrated in-situ and laboratory based approach

Sivakumar,

Jatin,

Mangnani

et al. 2024

Nat Hazards

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A Probabilistic Framework to Evaluate Seismic Resilience of Hospital Buildings Using Bayesian Networks

Cited by 37 publications

References 63 publications

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

Seismic Resilience of Emergency Departments: A Case Study

Seismic disaster resilience by improving infrastructure strength in active seismotectonic zones of Sikkim Himalaya, India—An integrated in-situ and laboratory based approach

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