Aibek Musaev, De Wang, & Calton Pu. (2014). LITMUS: Landslide detection by integrating multiple sources. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 677–686). University Park, PA: The Pennsylvania State University.
Abstract: Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS' functionality can be used to determine landslides related to the recent Typhoon Haiyan.
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Aibek Musaev, Kimberly Stowers, & Jonghun Kam. (2018). Harnessing Data to Create an Effective Drought Management System. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 544–552). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Drought is a complex climate phenomenon with slow emergence and quick vanish, which makes it hard for stakeholders to respond to drought timely. To reduce the vulnerability of our society to future drought, a better understanding of how society responds to drought is critical. Here, we propose a pilot study about social response to a recent California drought through social media. In this study, we identify the most important users using an extension of PageRank algorithm. We investigate the key drivers of the public activity in February, 2014 during the California drought. We also create a word cloud visualization from the most retweeted tweets. Lastly, we specify the information sources from those tweets. The findings of this study inform us that big data can help us to improve the current drought response plans through fundamental understanding of social response to drought, which is applicable to other natural hazards.
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Songhui Yue, Jyothsna Kondari, Aibek Musaev, Songqing Yue, & Randy Smith. (2018). Using Twitter Data to Determine Hurricane Category: An Experiment. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 718–726). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.
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