Heavy Rain Event in Japan: Difference between revisions
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|Developed technology=yes | |Developed technology=yes | ||
|Used platforms=Twitter | |Used platforms=Twitter | ||
|Long description=Overview: On July 5, record-breaking rainfall caused extensive damages in Kyushu and Chubu regions (southwest and central regions, Japan). Social media posts requesting rescue and safety confirmation were highlighted by people who had been isolated due to landslides and other damages. Oita Prefecture, one of the regions affected by the disaster, was able to reply to the social media posts from the disaster-stricken areas, using a Twitter-based AI Disaster Risk Management Solution. This system was used to collect messages from residents in risk situations during the disaster, and its use was effective for confirming damage conditions and providing appropriate contact information, which was used for multiple rescues. | |Long description=Overview: On July 5, 2020, record-breaking rainfall caused extensive damages in Kyushu and Chubu regions (southwest and central regions, Japan). Social media posts requesting rescue and safety confirmation were highlighted by people who had been isolated due to landslides and other damages. Oita Prefecture, one of the regions affected by the disaster, was able to reply to the social media posts from the disaster-stricken areas, using a Twitter-based AI Disaster Risk Management Solution. This system was used to collect messages from residents in risk situations during the disaster, and its use was effective for confirming damage conditions and providing appropriate contact information, which was used for multiple rescues. | ||
HIGHLIGHTED CASE: Some families have been saved by the use of this AI solution. | HIGHLIGHTED CASE: Some families have been saved by the use of this AI solution. | ||
Using the AI solution, officials from Oita Prefecture found a post of a resident who had become isolated after a road was blocked by a landslide. In one notorious case, residents of a family in the area were unable to contact the authorities, because a utility pole had fallen down and his landline phone was out of service, and cellular phone lines were also offline; while they managed to get an Internet connection, they posted on Twitter: "Mudslide right behind the evacuation center. Power outage, roads blocked, river flooding, we are isolated. It was too hard to evacuate with a one-and-a-half-year-old and a pregnant woman." | |||
After Oita Prefecture officials used the AI solution to find the above message successfully, the government immediately used the official Twitter account to send a reply, giving the contact information (phone number) for the disaster headquarters. Then, residents who received the reply informed the prefecture of their family's condition and the current situation in the area. Afterward, the prefecture shared information with the local fire department. In response, fire department officials were dispatched to ensure the safety of those residents. | |||
As subsequent reports, these residents said, according to local news, "At the time, I had a family member who was seven months pregnant. We also had young children and wanted someone to know what was going on. We wanted someone to find and help us in this difficult and stranded situation. That's how I felt when I posted this". Three months after the disaster, they gave birth safely, and the mother said, "I am grateful to the prefectural government for catching our social media postings and connecting us to support." Also, the official from the Oita Prefecture, who used AI solution to find the above tweet posted by these residents, said, "We in government do not have many opportunities to actually interact with residents. Social media is the most significant because it allows us to get detailed information from people on the site". | |||
|SMCS usage problems solving=Crisis information collection, notification, visualization and forecasting | |SMCS usage problems solving=Crisis information collection, notification, visualization and forecasting | ||
|Use cases thematic=Collecting and Analysing Information from SMCS, Ensuring Credible Information, Making Information Accessible, Mobilising Citizens, Targeting Communication | |Use cases thematic=Collecting and Analysing Information from SMCS, Ensuring Credible Information, Making Information Accessible, Mobilising Citizens, Targeting Communication | ||
Revision as of 05:41, 27 March 2023
Last edited: 23 October 2023
Hazard:
Flooding, LandslideYear:
2020Location:
Kyushu and Chubu regions, JapanScale:
CountryPublishing Organisation
unknown
Category
Real-world
Theme
Crowdsourcing, Social Media
Thematic
- Collecting and Analysing Information from SMCS
- Ensuring Credible Information
- Making Information Accessible
- Mobilising Citizens
- Targeting Communication
Disaster Management Phase
During
Description
Overview: On July 5, 2020, record-breaking rainfall caused extensive damages in Kyushu and Chubu regions (southwest and central regions, Japan). Social media posts requesting rescue and safety confirmation were highlighted by people who had been isolated due to landslides and other damages. Oita Prefecture, one of the regions affected by the disaster, was able to reply to the social media posts from the disaster-stricken areas, using a Twitter-based AI Disaster Risk Management Solution. This system was used to collect messages from residents in risk situations during the disaster, and its use was effective for confirming damage conditions and providing appropriate contact information, which was used for multiple rescues.
HIGHLIGHTED CASE: Some families have been saved by the use of this AI solution.
Using the AI solution, officials from Oita Prefecture found a post of a resident who had become isolated after a road was blocked by a landslide. In one notorious case, residents of a family in the area were unable to contact the authorities, because a utility pole had fallen down and his landline phone was out of service, and cellular phone lines were also offline; while they managed to get an Internet connection, they posted on Twitter: "Mudslide right behind the evacuation center. Power outage, roads blocked, river flooding, we are isolated. It was too hard to evacuate with a one-and-a-half-year-old and a pregnant woman."
After Oita Prefecture officials used the AI solution to find the above message successfully, the government immediately used the official Twitter account to send a reply, giving the contact information (phone number) for the disaster headquarters. Then, residents who received the reply informed the prefecture of their family's condition and the current situation in the area. Afterward, the prefecture shared information with the local fire department. In response, fire department officials were dispatched to ensure the safety of those residents.
As subsequent reports, these residents said, according to local news, "At the time, I had a family member who was seven months pregnant. We also had young children and wanted someone to know what was going on. We wanted someone to find and help us in this difficult and stranded situation. That's how I felt when I posted this". Three months after the disaster, they gave birth safely, and the mother said, "I am grateful to the prefectural government for catching our social media postings and connecting us to support." Also, the official from the Oita Prefecture, who used AI solution to find the above tweet posted by these residents, said, "We in government do not have many opportunities to actually interact with residents. Social media is the most significant because it allows us to get detailed information from people on the site".What was the overall goal of the Use Case?
Crisis information collection, notification, visualization and forecastingWhat limitations were identified?
Not all prefectures and municipalities have resources such as the AI solution mentioned above. To summarize the issues identified as overall trends in all regions affected by this disaster, in Kyushu and Chubu (southwest and central regions, Japan), in social medias such as Twitter, people affected by the disaster can find a way to request rescue using the hashtag #救助 (kyujyo, meaning "rescue" in Japanese) if they are unable to make a phone call. Therefore, when trying to find information from #救助, tweets unrelated to rescue requests were lined up, "burying" important information.
Researchers from the International Research Institute of Disaster Science at Tohoku University (IRIDeS) analyzed 1,058 tweets in which #救助 was used and found that only 2% of the tweets were presumed to have come from the disaster area, while the rest were from outside the disaster area and were not urgent. In addition, when some news media tweeted articles calling for the use of this hashtag, they used #救助 as it is, which caused recipients to retweet the articles in order to spread them, thus contributing to the "burying" of information. The IRIDeS researchers concluded, "The challenge is to improve the social media manners of people outside the affected areas. Also, the news media should be aware of the magnitude of their influence." After these facts, local news medias has emphasized that the hashtag #救助 is likely to be used as a "last recourse" for communication from disaster-stricken areas when telephone service is not available. Also, the Rescue-dedicated page on Twitter urges people not to spread the hashtag unnecessarily, but to call emergency services (119 in Japan).
Another limitation is that some types of landslides may occur without any anomalies in prior observations, leaving families and residents stranded. In such cases, information gathering and provision of reports by the surrounding community will be limited. In situations such as these, information based on scientific forecasts becomes important. In fact, it is possible to predict the risk and extent of landslides based on information released by the Meteorological Agency as well as geological surveys (especially those based on simulations using topography, geology, and rainfall data). Although such forecast information should be interpreted carefully as it contains uncertainties, it should be as accurate as possible using the latest technology. It is worth mentioning, finally, that it is crucial to understand the risk of landslides, to take appropriate countermeasures in advance, and in the event of a disaster, actions must be based on scientific forecasting information.