DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

Abstract

Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief activities in the aftermath of a disaster. These response systems require timely and accurate information about affected areas. In recent years, social media has provided access to high-volume real-time data, which can be used for advanced solutions to numerous problems, including disasters. Social-media data combines two modalities (text and associated images), and this information can be used to detect disasters, such as floods. This paper proposes an ensemble learning-based Deep Social Media Data Classification (DeepSDC) approach for social-media flood-event classification. The proposed algorithm uses datasets from Twitter to detect the flooding event. The Deep Social Media Data Classification (DeepSDC) uses a two-staged ensemble-learning approach which combines separate models for textual and visual data. These models obtain diverse information from the text and images and combine the information using an ensemble-learning approach. Additionally, DeepSDC utilizes different augmentation, upsampling and downsampling techniques to tackle the class-imbalance challenge. The performance of the proposed algorithm is assessed on three publically available flood-detection datasets. The experimental results show that the proposed DeepSDC is able to produce superior performance when compared with several state-of-the-art algorithms. For the three datasets, FRMT, FCSM and DIRSM, the proposed approach produced F1 scores of 46.52, 92.87, and 92.65, respectively. The mean average precision (MAP@480) of 91.29 and 98.94 were obtained on textual and a combination of textual and visual data, respectively.