Abstract
The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.
Highlights
The model can automatically select microblogs related to disasters from a large number of microblog messages, and further subdivide them into different types of disasters, which is different from the general classification system, so as to facilitate subsequent emergency response and loss estimation
The results showed that the deep neural network, especially Convolutional Neural Network (CNN), were more effective in identifying informative tweets
This paper first studied the characteristics of Weibo data related to typhoon disasters, and established a classification system of typhoon disaster information which met the characteristics of Weibo data
Summary
The VGI played an important role that could not be achieved by traditional methods from disaster awareness, information identification, and information classification to disaster determination [7,8,9]. Since 2012, during the period of each typhoon with a serious impact, the netizens in the affected areas published more than 100,000 microblog messages, including locations, winds, rainfalls, secondary disasters, rescue, and other related disaster information, with strong instantaneity and interaction. This paper established a classification system that meets the needs of typhoon emergency response based on the characteristics of microblog data, and constructed a generic typhoon disaster information automatic acquisition model using a neural network method. The advantages of the model included a wide application range, high reliability, strong pertinence, and fast speed
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