An Emergency Event Detection Ensemble Model Based on Big Data

Abstract

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as “Twitter” and “Snapchat”, while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time.

Metaverse Technology Research in Tourism and Hospitality Marketing in Indonesian Context: A Systematic Review

“…The significant presence of experimental design (N=8;23%) across all reviewed studies indicates a growing interest in conducting controlled experiments or empirical studies to assess the effectiveness, efficiency, and usability of MT applications in real-world THMK scenarios (Satriawan et al 2022;Alfalqi et al 2022;Arifitama et al 2019;Fendriani et al 2022;Kawato et al n.d.;Safitri et al 2022). This trend suggests a desire among researchers to empirically test hypotheses, validate theoretical frameworks and generate actionable insights to inform MT solutions within THMK context.…”

“…Lastly big data analytics appear to be content dimension in MT within THMK context represented by two publications (N=2;6%) dedicated to explore its implications (Alfalqi & Bellaiche, 2022;Maulana et al, 2012). Even though the studies did not explicitly explore the application of big data analytics within THMK context but the application from other fields signifies its implications in THMK context if effectively utilized.…”

Optimized Ensemble Approach for Multi-model Event Detection in Big data

“…Ebtesam Alomari et al [23] introduced Iktishaf for detecting traffic based events from Twitter information in Saudi Arabia. It constructs numerous classifiers with three machine learning (ML) techniques to detect eight various types of events.…”

“…Still, the performance of event detection can be enhanced further utilizing an improved form of schemes. Alan D. Smith et al [27] presented a framework for the event detection in educational big data records. The education oriented event detection considers the R-tree based, query based big data analysis model.…”