A novel emergency situation awareness machine learning approach to assess flood disaster risk based on Chinese Weibo
- PDF / 1,610,343 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 66 Downloads / 158 Views
(0123456789().,-volV)(0123456789(). ,- volV)
S.I. : WORLDCIST’20
A novel emergency situation awareness machine learning approach to assess flood disaster risk based on Chinese Weibo Hua Bai1 • Hualong Yu2 • Guang Yu2 • Xing Huang3 Received: 17 July 2020 / Accepted: 27 October 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Social media emerged as an important resource of information to improve the emergency situation awareness of flooding disasters. However, the online microblog text stream is unstructured and unbalanced obviously. Given the big, real-time, and noisy flood disaster microblog text flow, a new regional emergency situation awareness model to automatic assess flood disaster risk is proposed. Firstly, according to the established online disaster event-meta frame, a multi-label classification algorithm for the flood microbloggings is constructed based on the historical dataset. This algorithm helps to assign the relevant event-meta tags to each situation microbloggings. Second, a new machine learning method for dynamic assessment of flood risk for online microbloggings is developed. The flood event-metas are considered to be feature vectors, and the four different levels of flood risk are considered to be four classes. Then, the flood risk assessment task is innovatively transformed into a multi-classification task. By the logistic regression ordered multi-classification algorithm, the dynamic quantitative evaluation of event-meta, users and regional risks is realized. Finally, the proposed model is applied in the case of the Yuyao Flood. The results of the case study show that the Yuyao Flood’s online quantitative risk assessment results are consistent with real accumulated precipitation data, which illustrate that the proposed machine learning model could realize the bottom-up automatic disaster information collecting by processing victim user-generated content effectively. Social media is proven to supplement the deficiencies of traditional disaster statistics and provide realtime, scientific information support for the implementation of flood emergency processes. Keywords Flood risk assessment Social media Emergency situation awareness Machine learning
1 Introduction Globally, flood disasters affect more people than any other natural disaster. Flood disasters have the typical characteristics of strong suddenness, wide affected areas, and large casualties and property losses. After entering the twenty-first century, as global warming strengthens and the urbanization process accelerates, the frequency and impact of flood disasters increase. For example, the Yuyao Flood & Xing Huang [email protected] 1
School of Economics, Fujian Normal University, Fuzhou, China
2
School of Management, Harbin Institute of Technology, Harbin, China
3
School of Economics and Management, Southwest University of Science and Technology, Mianyang, China
(2013, Zhejiang, China) and the Shouguang Flood (2018, Shandong, China) caused huge material losses and casualties. Therefore, deali
Data Loading...
