Every activity in disaster management demands accurate and up-todate information to allow a quick, easy, and cost-efective response to reduce the possible loss of lives and properties. It is a challenging and complex task to acquire information from diferent regions of a disaster-afected area in a timely fashion. The extensive spread and reach of social media and networks such as Twitter allow people to share information in real-time. However, gathering of valuable information requires a series of operations such as (1) processing each tweet for the text classiication, (2) possible location determination of people needing help based on tweets, and (3) priority calculations of rescue tasks based on the classiication of tweets. These are three primary challenges in developing an efective rescue scheduling operation using social media data. In this paper, irst, we propose a deep learning model combining attention based Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) to classify the tweets. Next, we perform feature engineering to create an auxiliary feature map which dramatically increases the model accuracy. In our experiments using data from Hurricanes Harvey and Irma, it is observed that our proposed approach performs better compared to other classiication methods based on Precision, Recall, F1-score, and Accuracy, and is highly efective to determine the priority of a tweet. Furthermore, to evaluate the efectiveness and robustness of the proposed classiication model a merged dataset comprises of 4 diferent datasets from CrisisNLP and another 15 diferent disasters data from CrisisLex are used. Finally, we develop an adaptive multi-task hybrid scheduling algorithm considering resource constraints to perform an efective rescue scheduling operation considering diferent rescue priorities.
M. Y. Kabir and S. K. Madria, "A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Management," GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, pp. 269-278, Association for Computing Machinery (ACM), Nov 2019.
The definitive version is available at https://doi.org/10.1145/3347146.3359097
27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 (2019: Nov. 5-8, Chicago, IL)
Keywords and Phrases
Deep Learning; Disaster management; Neural Network; Priority Determination; Rescue Scheduling; Social Media
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 The Authors, All rights reserved.
01 Nov 2019