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
References
Albrecht C, Elmegreen B, Gunawan O et al (2020) Next-generation geospatial-temporal information technologies for disaster management. IBM J Res Dev 64:1–12. https://doi.org/10.1147/JRD.2020.2970903
Al-Dahash H, Kulatunga U, Thayaparan M (2019) Weaknesses during the disaster response management resulting from war operations and terrorism in Iraq. Int J Disaster Risk Reduct 34:295–304. https://doi.org/10.1016/j.ijdrr.2018.12.003
Aqib M, Mehmood R, Alzahrani A, Katib I (2020) A Smart Disaster Management System for Future Cities Using Deep Learning, GPUs, and In-Memory Computing. 159–184. https://doi.org/10.1007/978-3-030-13705-2_7
Asencio-Cortés G, Florido E, Troncoso A, Martínez-Álvarez F (2016) A novel methodology to predict urban traffic congestion with ensemble learning. Soft Comput 20:4205–4216. https://doi.org/10.1007/s00500-016-2288-6
Avvenuti M, Cresci S, Del VF, Tesconi M (2017) On the need of opening up crowdsourced emergency management systems. Ai Soc 33:55–60. https://doi.org/10.1007/s00146-017-0709-4
Barber D (2011) Bayesian reasoning and machine learning, 1 st. Cambridge University Press, United Kingdom
BayesFusion L (2020) SMILE Engine – artificial intelligence modeling and machine learning software based on Bayesian networks. https://www.bayesfusion.com/smile/
Bielza C, Larrañaga P (2014) Discrete bayesian network classifiers: A survey. ACM Comput Surv 47:. https://doi.org/10.1145/2576868
Borovcnik M, Bentz H-J, Kapadia R (2011) A Probabilistic Perspective
Chen J, Chen H, Wu Z et al (2016) Forecasting smog-related health hazard based on social media and physical sensor. Inf Syst 64:281–291. https://doi.org/10.1016/j.is.2016.03.011
Chiappa S, Isaac WS (2019) A causal bayesian networks viewpoint on fairness. IFIP advances in information and communication technology. Springer, New York LLC, pp 3–20
Delen D, Topuz K, Eryarsoy E (2019) Development of a bayesian belief network-based DSS for predicting and understanding freshmen student attrition. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2019.03.037
Developer.twitter.com (2018) Twitter Developer
Flouris I, Giatrakos N, Deligiannakis A et al (2017) Issues in complex event processing: status and prospects in the big data era. J Syst Softw 127:217–236. https://doi.org/10.1016/j.jss.2016.06.011
Groen FCA, Pavlin G, Winterboer A, Evers V (2017) A hybrid approach to decision making and information fusion: combining humans and artificial agents. Rob Auton Syst 90:71–85. https://doi.org/10.1016/j.robot.2016.08.009
Institute for Economics & Peace (2018) Measuring the impact of terrorism
Jimenez-Marquez JL, Gonzalez-Carrasco I, Lopez-Cuadrado JL, Ruiz-Mezcua B (2019) Towards a big data framework for analyzing social media content. Int J Inf Manage 44:1–12. https://doi.org/10.1016/j.ijinfomgt.2018.09.003
Johndrow JE, Smith A, Pillai N, Dunson DB (2019) MCMC for imbalanced categorical data. J Am Stat Assoc 114:1394–1403. https://doi.org/10.1080/01621459.2018.1505626
Kim T, Ko W, Kim J (2019) Analysis and impact evaluation of missing data imputation in day-ahead PV generation forecasting. Appl Sci 9:1–18. https://doi.org/10.3390/app9010204
Kuhn M, Johnson K (2013) Applied predictive modeling
Kumar A, Singh JP (2019) Location reference identification from tweets during emergencies: a deep learning approach. Int J Disaster Risk Reduct 33:365–375. https://doi.org/10.1016/j.ijdrr.2018.10.021
Kuseng N, Muangsuk S, Desulong W (2019) Deep south watch database
Li S, Chen S, Liu Y (2019) A method of emergent event evolution reasoning based on ontology cluster and bayesian network. IEEE Access 7:15230–15238. https://doi.org/10.1109/ACCESS.2019.2894777
Madu CN, Kuei C, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1–17. https://doi.org/10.1016/j.scs.2016.12.012
Makenzie D, Pearl J (2018) The book of why: the new science of cause and effect. Basic Books 402
Martinez-Hernandez U, Dehghani-Sanij AA (2019) Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor. Pattern Recognit Lett 118:32–41. https://doi.org/10.1016/j.patrec.2018.03.020
Mazhar Rathore M, Ahmad A, Paul A et al (2017) Advanced computing model for geosocial media using big data analytics. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4644-7
Mujalli RO, López G, Garach L (2016) Bayes classifiers for imbalanced traffic accidents datasets. Accid Anal Prev 88:37–51. https://doi.org/10.1016/j.aap.2015.12.003
Murphy K (2001) An introduction to graphical models. Rap Tech 96:1–19. https://doi.org/10.1109/VTCFall.2014.6965847
Neri F (2016) Linear algebra for computational sciences and engineering. Springer, Cham
O’Hagan A, Buck CE, Daneshkhah A et al (2006) Uncertain judgements: eliciting experts’ probabilities. John Wiley & Sons, Ltd, Chichester, UK
Pearl J (1986) Fusion, propagation, and structuring in belief networks. Artif Intell 29:241–288
Pearl J (2009) Causality: models, reasoning and inference, 2nd edn. Cambridge University Press
Pearl J (2019) The seven tools of causal inference, with reflections on machine learning. Commun ACM 62:54–60. https://doi.org/10.1145/3241036
Sahoh B, Choksuriwong A (2017) Smart emergency management based on social big data analytics: research trends and future directions. In: Proceedings of the 2017 international conference on information technology—ICIT 2017. pp 1–6
Sahoh B, Choksuriwong A (2020) Automatic semantic description extraction from social big data for emergency management. J Syst Sci Syst Eng 29:412–428. https://doi.org/10.1007/s11518-019-5453-5
Sit MA, Koylu C, Demir I (2019) Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma. Int J Digit Earth. https://doi.org/10.1080/17538947.2018.1563219
Song G, Khan F, Yang M (2019) Probabilistic assessment of integrated safety and security related abnormal events: a case of chemical plants. Saf Sci 113:115–125. https://doi.org/10.1016/j.ssci.2018.11.004
Stylianou K, Dimitriou L, Abdel-Aty M (2019) Big data and road safety: a comprehensive review. Elsevier Inc.
Sukmaningsih DW, Suparta W, Trisetyarso A (2020) intelligent information and database systems: recent developments. Springer International Publishing
Tang Z, Li Y, Hu X, Wu H (2019) Risk analysis of urban dirty bomb attacking based on Bayesian network. Sustain 11:1–12. https://doi.org/10.3390/su11020306
Varshney D, Kumar S, Gupta V (2017) Predicting information diffusion probabilities in social networks: a Bayesian networks based approach. Knowledge-Based Syst 133:66–76. https://doi.org/10.1016/j.knosys.2017.07.003
Wang Y, Gao H, Chen G (2017) Predictive complex event processing based on evolving Bayesian networks. Pattern Recognit. Lett.
Wu B, Tian H, Yan X, Guedes Soares C (2019) A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks. Proc Inst Mech Eng Part O J Risk Reliab. https://doi.org/10.1177/1748006X19825706
Yu M, Huang Q, Qin H et al (2019) Deep learning for real-time social media text classification for situation awareness–using Hurricanes Sandy, Harvey, and Irma as case studies. Int J Digit Earth 1–18. https://doi.org/10.1080/17538947.2019.1574316
Zarei E, Khakzad N, Cozzani V, Reniers G (2019) Safety analysis of process systems using Fuzzy Bayesian Network (FBN). J Loss Prev Process Ind 57:7–16. https://doi.org/10.1016/j.jlp.2018.10.011
Zhang H, Li Y, Zhang H (2019) Risk early warning safety model for sports events based on back propagation neural network machine learning. Saf Sci 118:332–336. https://doi.org/10.1016/j.ssci.2019.05.011
Zhao X, Banks A, Sharp J et al (2020) A Safety framework for critical systems utilising deep neural networks. arXiv Prepr arXiv200305311
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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