Japanese literature organization and spatiotemporal database system creation for natural disaster analysis

Author:

Lyu Bing,Yue Xuebin,Meng Lin

Abstract

AbstractJapan is one of the countries with the most frequent natural disasters in the world and is faced with various threats of natural disasters every year, which significantly impact Japan’s social economy and people’s lives. A great deal of information about disasters is preserved in Japanese literature. Interpreting and organizing this information help us to analyze the regularity of disasters and understand the preventive measures of ancient people. This paper aims to organize, analyze and save disaster data by collecting various information about disasters. Then a disaster spatiotemporal database system is constructed by using deep learning, image processing, and database technology. The system consists of two parts, namely, the disaster database and disaster website. The disaster database is the core of the whole system, which saves the disaster data after organizing and summarizing. The database collects disaster information from various sources, including key information such as disaster type, time, location, scale, and scope of impact. The Disaster website is the system’s user interface, providing an interactive platform for users to access and use disaster data easily. The website has many functions, including search, visual display, disaster information query, etc. We also make a detailed analysis of the collected data, aiming to predict the causes and occurrence rules of disasters so as to achieve the target of disaster prediction.

Publisher

Springer Science and Business Media LLC

Subject

Archeology,Archeology,Conservation,Computer Science Applications,Materials Science (miscellaneous),Chemistry (miscellaneous),Spectroscopy

Reference39 articles.

1. A Report of Japan Institute of Country-ology and Engineering. https://www.jice.or.jp/knowledge/japan/commentary09

2. A Report of Disaster Management in Japan. https://www.bousai.go.jp/kohou/kouhoubousai/h23/63/special_01.html

3. Nihonshoki. https://www.archives.go.jp/exhibition/digital/rekishitomonogatari/contents/02.html

4. Kojiki. https://www.archives.go.jp/exhibition/digital/rekishitomonogatari/contents/01.html

5. Imran M, Elbassuoni S, Castillo C, Diaz F, Meier P. Practical extraction of disaster-relevant information from social media. In: Proceedings of the 22nd International Conference on World Wide Web, 2013; pp. 1021–1024.

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