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The Disaster Risk Reduction Knowledge Service System of IKCEST introduced in 2018 China Conference on Geography

2018-09-13  |   Editor : houguangbing  
Category : News

Abstract

IKCEST Disaster Risk Reduction Knowledge Service was presented in the 2018 China Conference on Geography (CCG 2018), held at Xi’an, 29-30 August 2018, and won the Excellent Thesis Award.

Content

Professor Juanle Wang, project technical director of the IKCEST Disaster Risk Reduction Knowledge Service, took part in China Conference on Geography 2018 hold in Xian city with 6 team members on 29th August 2018 . The conference was sponsored by Chinese Geographic Society and Shanxi Normal University. There were 3 team members that made presentations and one of them named Extraction and Analysis of Earthquake Events Information based on Web Text won the Best Thesis Award.

Fig 1 :Prof. Wang Juanle took part in CCG 2018 with team members

In the CCG 2018, Cheng Kai, Han Xuehua and Wang Mingming reported their papers respectively, and the topics of their papers were “Study on population data spatialization based on multi-source data—Taking Shandong province as an example”, “Study on forest types extraction oriented SDG15”, and “Extraction and Analysis of Earthquake Events Information based on Web Text”. Among them, “Extraction and Analysis of Earthquake Events Information based on Web Text” is a new study based on the IKCEST Disaster Risk Reduction Knowledge Service. This study explores the extraction and comparison of earthquake events information from web news media reports (news reports) and online disaster reduction agency reports (professional reports). Using earthquakes in China from 2015 to 2017 as a case study, a set of rules is created for extracting earthquake events information, including temporal extraction rules, a location trigger dictionary, and an attribute trigger dictionary. In particular, the differences in characteristics of earthquake events in news reports versus professional reports is analyzed performing by methods of web crawler, named entity recognition, geocoding, and kernel density estimation, in terms of their quantity and spatiotemporal distribution. The author of this paper, Han Xuehua won the Best Thesis Award.

Fig2 : the paper based on DRR won the Best Thesis Award.

Provided by the IKCEST Disaster Risk Reduction Knowledge Service System

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