Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data

Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event

Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.

TriggerCit: Early Flood Alerting using Twitter and Geolocation - a comparison with alternative sources

Employment of hydraulic model and social media data for flood hazard assessment in an urban city

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039703

A data-driven group emergency decision-making method based on interval-valued intuitionistic hesitant fuzzy sets and its application in COVID-19 pandemic

Whenever a natural disaster occurs, a damage assessment must be conducted to determine the extent of the damage caused, in order to quickly and effectively undertake disaster response, recovery, and reconstruction efforts. It is important to consider not only natural phenomena, but the impact of the damage on local communities as well (which is a pressing concern at any disaster site). Although a conventional, field-survey-based disaster assessment can yield solid information, it still takes time to gauge the overall implications. While an SNS system can facilitate information collection in real time, it is riddled with problems such as unreliability, and the challenge of handling vast amounts of data. In this study we analyzed Twitter content that was generated after the 2018 Hokkaido Eastern Iburi Earthquake and was related to disaster response efforts at the site of the disaster, and used it to test an approach that combines and utilizes natural language processing and geo-informatics for disaster assessment. We then verified the use of this process in two different disaster response scenarios. In this paper, we discuss some possible approaches to disaster assessment that utilize SNS information analysis technology.

An Attempt to Grasp the Disaster Situation of “The 2018 Hokkaido Eastern Iburi Earthquake” Using SNS Information

Earthquakes damage physical assets, such as houses, public infrastructure, fields, factories, facilities, as well as inventory of timbers, crops, or products. A direct damage amount is the primary evidence for financial measures to restore and reconstruct the affected areas. Therefore, from a policy perspective, it is essential to estimate it quickly and accurately. Cui et al. have proposed a simple method for estimating direct damages [1, 2]. This study aimed to build a prototype of automatic estimation system and discuss its social implementation. As a result, we succeeded in estimating three earthquakes – the 2018 Osaka Prefecture Northern Earthquake, 2018 Hokkaido Iburi Earthquake, and 2019 Yamagata-oki Earthquake – damage amounts automatically and defining some technical requirements for development. On the other hand, it is necessary to replace the Minryoku index, which is used for Cui’s estimation method and no longer being updated, by new physical assets quantity index, which is continuously updatable. Moreover, the estimation accuracy must be evaluated and improved in finer units of space.

Developing an Automated System for Simple Estimation of the Direct Damage Amounts from Earthquakes

After a natural disaster occurs, the production and sharing of damage reports are extremely important for a disaster response site. However, one of the problems is that the data shared by the damage reports cannot clearly indicate when the damage situation could be grasped because such data change day by day. Accordingly, in this study, the data of the damage reports of the Headquarters for Disaster Control are treated as unequally spaced time series data to evaluate the changing conditions of the data quantitatively. For this purpose, a case is examined for the Headquarters for Disaster Control of Fukuoka Prefecture at the time of the Northern Kyushu Heavy Rainfall event in July 2017. As a result of the examination, it is indicated that the quantitative evaluation would be possible for 1) analysis on timing when the data of the damage reports are updated, 2) analysis on the characteristics of time series of the report data, and 3) visualization of the progress of the damage report service.

Time Series Analysis on the Damage Report of the Northern Kyushu Heavy Rainfall in July 2017

This paper presents a rapid or real-time estimation method of the economic value of direct stock damages caused by significant earthquakes in Japan. The result will contribute to both the government and private sectors’ early decision-making, particularly for provisional budget allocation. First, we developed a simple but evidence-based model for estimating stock losses explained by a representative earthquake hazard factor and an exposure factor, i.e., seismic intensity and existing stock of physical assets. The key characteristic of our estimation model is that the dependent variable is prefectural damage amount. Still, the explanatory variables come from municipal sources: we overcome this data availability problem through our estimation process. Second, we carefully checked the model’s specification, estimation, and performance to be soundly applied to a real-time assessment of future earthquake events. We also explain the automated measuring of the prefectural direct loss value and its distribution to every 250 m mesh. Finally, we show two examples of the application of our model; one is the case of the 2018 Northern Osaka Earthquake, and the other is the anticipated Tokyo inland earthquake.

Qinglin Cui

Papers

Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data

An Attempt to Grasp the Disaster Situation of “The 2018 Hokkaido Eastern Iburi Earthquake” Using SNS Information

Developing an Automated System for Simple Estimation of the Direct Damage Amounts from Earthquakes

Time Series Analysis on the Damage Report of the Northern Kyushu Heavy Rainfall in July 2017

Rapid Estimation of Direct Economic Losses Caused by Significant Earthquakes: An Evidence-Based Model and its Applications