Abstract
The goals of emergency management are to restore human safety and security, and to help the authorities understand what causes such events. It requires information that is both highly accurate, and can be generated very quickly. This research addresses these concerns with a machine learning model based on cause-and-effect using a Bayesian belief network. This employs human critical thinking and amplified context to encode the model structures, which contribute towards its imitation of human-intelligent understanding, and the model parameters are fitted using social media data. The results show that our model is a natural fit for a real-world environment required emergency management.
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Abbreviations
- BBNs:
-
Bayesian Belief Networks
- DEU:
-
Deep Event Understanding
- ML:
-
Machine Learning
- 5W1H:
-
“Who”, “What”, “Where”, “When”, “Why”, and “How”
- CPT:
-
Conditional Probability Table
- TAN:
-
Tree Augmented Bayes
- BAN:
-
Augmented Naive-Bayes
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To highlight an innovative causal model based on social sensor to contribute the goals of the emergency management. To propose a state-of-the-art framework, combining Bayesian Belief Network for emergency knowledge and social sensor based on critical thinking (“Who”, “What”, “Where”, “When”, “Why”, and “How”). To propose an emergency causal model based on amplified, high levels of human interpretation. This links human critical thinking and social sensor-based emergency information. To prove that a causal model based on Bayesian Belief Network can provide a best fit model for emergency management.
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Sahoh, B., Choksuriwong, A. A proof-of-concept and feasibility analysis of using social sensors in the context of causal machine learning-based emergency management. J Ambient Intell Human Comput 13, 3747–3763 (2022). https://doi.org/10.1007/s12652-021-03317-3
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DOI: https://doi.org/10.1007/s12652-021-03317-3