Prediction of issuance of emergency evacuation orders for chemical accidents using machine learning algorithm

Phark, Chuntak; Kim, Whapeoung; Yoon, Yeo-Song; Shin, Gwy-Am; Jung, Seungho

doi:10.1016/j.jlp.2018.08.021

Cited by 19 publications

(10 citation statements)

References 3 publications

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“…Gurina et al 20 and Kurian et al 21 created a risk score calculation matrix, the input data is processed based on the probability, severity, consequences of the incident, and possible outcomes of an accident that can be predicted based on the risk scores. Phark et al, 47 Cakir et al 44 used CART, TAN, and ANN models to identify the causes behind the accidents using the data from various databases, the developed models have achieved an accuracy of 0.89 & 0.665, respectively. Since the class labeling, preprocessing, missing value handling, and class imbalance problems have an impact on the accuracy of the ML model performance, the data must be curated as per the models' requirements.…”

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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Summary Recent advancements in machine learning techniques are helping researchers to develop predictive models that assist decision‐makers to get a quick, unbiased overview of the processes. But studies using machine learning approaches in analyzing and classifying the injury narratives of the petroleum industries are still in their early stages due to data unavailability and lack of trust in these models. Comparatively, other industries such as construction, manufacturing, aviation and so forth are using the findings from the predictive models but the use of machine learning techniques in analyzing petroleum industry accident data is not gaining much importance. This study aims to use available accident data from the Indian petroleum industry to develop a classification model for predicting possible outcomes of an accident. The data used in this study comprises 194 accident reports with 20 information attributes collected during the 2016–20 period. Six different machine learning algorithms are used to analyze and classify the possible outcome of the accident. It has been found that the Xgboost algorithm has achieved 95% accuracy following multilayer perceptron with 94% accuracy. The rough set theory is also used to extract the indiscernibility relationship between the given attributes causing accident occurrence. The results indicate that “skill‐based error, supervisory violation, no personal protective equipment usage, and lack of standard operating procedure compliance” have contributed to the majority of the accidents. The findings of this study can be used to assist safety professionals in decision‐making, mitigating the root causes of contributing factors.

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Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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“…Due to its simplicity, efficiency, and rigor, the Bayesian classifier is one of the most widely used algorithms for classification. 22,23…”

Section: -1 Naive Bayes Classifiermentioning

confidence: 99%

Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire

Sahebi

Jahangiri

Alibabaei

et al. 2022

Disaster med. public health prep.

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Objective: A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest time possible. The aim of this study was to predict the duration of emergency evacuation following hospital fire using machine-learning algorithms. Methods: In this study, the real emergency evacuation duration of 190 patients admitted to a hospital was predicted in a simulation based on the following 8 factors: the number of hospital floors, patient preparation and transfer time, distance to the safe location, as well as patient’s weight, age, sex, and movement capability. To design and validate the model, we used statistical models of machine learning, including Support Vector Machines Random Forest, Naive Bayes Classifier, and Artificial Neural Network. Results: Data analysis showed that based on the Area Under the Curve, precision, and sensitivity values of 99.5%, 92.4%, and 92.1%, respectively, the Random Forest model showed a better performance compared to other models for predicting the duration of hospital emergency evacuation during fire. Conclusion: Predicting evacuation duration can provide managers with accurate information and true analyses of these events. Therefore, health policy makers and managers can promote preparedness and responsiveness during fire by predicting evacuation duration and developing appropriate plans using machine learning models.

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“…In this study, 70% of all data were used for training and 30% for testing purposes. All models were developed with RapidMiner Studio Version 9.3 software (Boston, USA), which has been used in many studies (Phark et al 2018;Cuesta et al 2019). RapidMiner Studio consists of operators and each operator has a task.…”

Section: Modelsmentioning

confidence: 99%

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

Bardak

Peker

et al. 2021

BioRes

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Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

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Prediction of issuance of emergency evacuation orders for chemical accidents using machine learning algorithm

Cited by 19 publications

References 3 publications

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

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