Title

STIMULATE: A System for Real-Time Information Acquisition and Learning for Disaster Management

Abstract

Real-Time information sharing and propagation using social media such as Twitter has proven itself as a potential resource to improve situational awareness in a timely manner for disaster management. Traditional disaster management systems work well for analyzing static and historical information. However, they cannot process dynamic streams of data that are being generated in real-Time. This paper presents STIMULATE-a System for Real-Time Information Acquisition and Learning for Disaster Management that can (1) fetch and process tweets in real-Time, (2) classify those tweets into FEMA defined categories for rescue priorities using pre-Trained deep learning models and generate useful insights, (3) find FEMA defined stranded people for rescue missions of varying priorities, and (4) provide an interactive web interface for rescue management given the available resources. The STIMULATE prototype is primarily built using the Python Flask framework for web interaction. Additionally, it is deployed in the cloud environment using Hadoop and MongoDB for scalable storage, and on-demand computing for processing extensive social media data. The deep learning models in the STIMULATE prototype use Python Keras and the TensorFlow library. We use Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) for developing the tweet classifier. Further, we use the Python PyWSGI WebSocket server for rescue scheduling operations. We present a deep learning system trained on hurricane Harvey and Irma datasets only. The tweet classifier is evaluated using 15 different disaster datasets. Finally, we present the results of multiple simulations using synthetic data with different sizes to measure the performance and effectiveness of the tweets processor and rescue scheduling algorithm.

Meeting Name

IEEE International Conference on Mobile Data Management

Department(s)

Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Deep learning; Disaster management; Real-Time system; Rescue scheduling; Social media

International Standard Book Number (ISBN)

978-172814663-8

International Standard Serial Number (ISSN)

1551-6245

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 2020

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