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.
A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data
