  It is a challenging and complex task to acquire information from different
regions of a disaster-affected area in a timely fashion. The extensive spread
and reach of social media and networks allow people to share information in
real-time. However, the processing of social media data and gathering of
valuable information require a series of operations such as (1) processing each
specific tweet for a text classification, (2) possible location determination
of people needing help based on tweets, and (3) priority calculations of rescue
tasks based on the classification of tweets. These are three primary challenges
in developing an effective rescue scheduling operation using social media data.
In this paper, first, 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 under different categories. We use
pre-trained crisis word vectors and global vectors for word representation
(GLoVe) for capturing semantic meaning from tweets. Next, we perform feature
engineering to create an auxiliary feature map which dramatically increases the
model accuracy. In our experiments using real data sets from Hurricanes Harvey
and Irma, it is observed that our proposed approach performs better compared to
other classification methods based on Precision, Recall, F1-score, and
Accuracy, and is highly effective to determine the correct priority of a tweet.
Furthermore, to evaluate the effectiveness and robustness of the proposed
classification model a merged dataset comprises of 4 different datasets from
CrisisNLP and another 15 different disasters data from CrisisLex are used.
Finally, we develop an adaptive multitask hybrid scheduling algorithm
considering resource constraints to perform an effective rescue scheduling
operation considering different rescue priorities.
