Journal of Natural Disaster Science, Volume 40，Number 1，2019，pp1-13

Detecting flood inundation information through Twitter:
The 2015 Kinu River flood disaster in Japan
Yongxue Shi1, Takahiro Sayama2, Kaoru Takara 3 and Kiyonori Ohtake4
1

Graduate School of Engineering, Kyoto University, Japan
Disaster Prevention Research Institute, Kyoto University, Japan
3
Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, Japan
4
National Institute of Information and Communications Technology, Japan
2(Received: Oct. 26, 2018

Accepted: Mar. 5, 2019)

Abstract
For effective responses to flood disasters, it is essential to identify affected areas in real time.
Recently, social media (e.g. Twitter) have emerged as new sources of disaster-related informationin real time. However, concerns still remain regarding the trustworthiness and the amount of
information, especially that issued from a site of crisis. This study investigated a total of 109
tweets sampled based on certain criteria during a flood disaster in the Kinu River Basin, Japan in
September 2015. We classified them into five categories depending on the main contents: 1)
flood inundation, 2) rescue, 3) emotion, 4) river condition, and 5) damage situation. The analysis
suggests that the highest proportion (37%) of tweets were related to flood inundation followed bydamage (19%) and 32% of them were posted in near real time with photos. We further comparedwell-positioned tweets with other inundation extent information based on our field investigations
and aerial photos. The results showed good agreement between the inundation information fromthe posted tweets and the expected locations. Some tweets suggested additional inundated areas,
not originally identified by the aerial photos. Overall, the study shows the potential use of social
media to collect local details about floods.
Keywords: The Kinu River Flooding; Flood risk; Social media; Twitter

1. Introduction
On September 10, 2015, due to heavy rainfall over three days, an overtopping flood occurredalong the Kinu River in Japan around 6:00 AM. At 12:50 PM on the same day, a levee breachalso occurred downstream. This disaster left two people dead and several injured with enormous
damage to homes and infrastructure in Joso City. For effective countermeasures, real-time
information on the local flood situation is indispensable. Real-time flood hazard maps could offer
a better view on a crisis, however, the mapping has not yet been realized. The challenge to create
them is how to determine the extent of a flood on a real-time basis.
There are currently three ways to evaluate the extent of damage: remote sensing,computer simulations, and collecting details on local flood inundations in affected areas. In thecase of the Kinu River flood disaster, Japan’s Geospatial Information Authority (GSI) took aerial
photos from helicopters several hours after the flood to measure the area of the flood zone.
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Y.SHI, T.SAYAMA, K.TAKARA, K.OHTAKE

Although GSI operated a helicopter after the flood occurrence and used the photos to assess how
far it had spread, GSI could not pinpoint specific locations of flood inundation during its spread.
Regarding computer simulations, it is still difficult to set inundation parameters in real time,
moreover, the accuracy of the inundation model is also a challenge for real-time simulation.
Social media is used extensively for sharing crisis information (Keim and Noji 2011; Choi
and Bae 2015; Cameron et al. 2012; Sutton et al. 2008). The first example of intensive uses ofSocial media during a crisis was a coordinated suicide attack in London in the UK on July 7,
2005. People distributed photographs and videos recorded with mobile media across Flicker andWikipedia to spread the news about the reality on the ground (Lorenzo-Dus and Bryan 2011; Li etal. 2013). Compared to ordinary situations, the amount of tweets increases during disasters: 3.28
million tweets were posted related to the Haiti Earthquake in 2010, 33 million tweets were posted
related to the Great East Japan Earthquake in 2011, 20 million tweets were posted in the case of
Hurricane Sandy in 2012, and 5.72 million tweets were posted in Typhoon ‘Haiyan’ in 2013
(Sarcevic et al. 2012; BIGLOBE 2011; Olanoff 2012; Levine 2013; Kongthon et al. 2012; Ntalla
et al., 2015).
There are three main methods of social media usage for disaster response. The first usage
is disaster occurrence detection, which typically utilizes an online system to monitor the rapid
increase of specific keywords on the Internet (Choi and Bae 2015; Bee 2012; Kumar 2011;
Emmanouil and Nikolaos 2015). The second usage is to call for rescue during disasters. Althoughsocial media has been utilized to post-rescue requests in recent disasters, there are still some
intrinsic issues for decision making in rescuing when replying on social media, identifying only
critical rescue requests is a particularly significant challenge (Sato 2017). The third usage is to
identify hazard and damage situations. The reported examples include an earthquake intensitymap (Paul et al. 2010); flood inundation identifications (Smith 2015; Fohringer 2015; Eilander2016). The applications expect that on-site residents can be active distributors of disaster-related
information by posting real-time information on the situations (Spinsanti and Ostermann 2013).
Among the three usages, this study focuses on the third usage by taking the example ofthe Kinu River flooding in 2015. In particular, the objectives of the study are: 1) to investigate themain messages in tweets posted during the Kinu River Flooding by manually classifying thetweets posted into five categories including flood inundation, rescue, emotion, river condition,and damage information and 2) to verify if flood inundation-related tweets agree on the actual
flood situation both in time and space.

2. Methodology
2.1 Data harvesting
We used the following three methods to collect tweets on the Kinu River flood disaster：a) Twitter’s advanced search function, b) Twitter Application Interface (API) and c)
DISAster-information ANALyzer (DISAANA) (Ohtake 2015). The general rules on collecting
tweets can be summarized as follows: 1) We focus only on the ones tweeted on the same day as
the disaster occurred i.e. from 0:00 to 24:00 on September 10, 2015; 2) We used the following
keywords for searching the tweets on the flooding: inundation, levee breach, rescue, typhoon,
flood and disaster in Japanese words; 3) to identify the specific event, we used each keyword
together with “Kinugawa” and we set “news” as an excluding keyword to eliminate Twitter
information from mass media; 4) Since there are still too many uninformative tweets, we2Journal of Natural Disaster Science, Volume 40，Number 1，2019，pp1-13

manually selected tweets based on the following criteria: flood information by Twitter users who
observed levee overtopping directly, inundation and structural damage; tweets that call for rescue
or offer information on evacuation (e.g. updating information on rescue team activities);
information representing the sender’s feelings or emotions on the target flood (e.g. fear of apotential flood).
a) Twitter’s advanced search function
We used Twitter’s advanced search function by employing the above keywords and tweeted time.
The advanced search function is the easiest to access from the Internet.
b) Twitter Application Programming Interface (APIs)
Twitter provides search services based on its own API. We utilized Twitter’s REST API to searchfor disaster-related tweets. REST APIs can set both keywords, time, and allow users to detect
tweets posted in a specific time (Twitter 2013). We set the time on September 10, 2015, to obtaintweets related to the Kinu River flooding with the same keywords and criteria set as describedabove. All the programming and usage of Twitter API follow the rules and guidelines of Twitter.
c) DISAster–information ANAlyzer (DISAANA)DISAANA is an online system that picks up and locates tweets related disasters and accidents inreal time (Ohtake 2015). DISAANA instantly extracts and presents answer candidates when asimple question is input. In this research, the DISAANA system prepared particularly for the
Kinu River flooding was used to detect tweets. DISAANA provides two modes: problem-listing
mode and question-answering mode. In this study, we used the problem-listing mode by setting
the same keywords to obtain related tweets.

Figure1 Examples of searches when inputting a simple question into DISAANAAs a result, 109 tweets were harvested by those three methods by follwing the rules
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Y.SHI, T.SAYAMA, K.TAKARA, K.OHTAKE

2.2 Data analysis
To understand the detailed contents of the 109 selected tweets, we analyzed them from thefollowing viewpoints: 1) the main messages in the content, 2) if the content is related to the user
or to others, 3) if tweets were posted in real time, 4) if tweets related to inundation agree with theactual situation centers, shelters and evacuation advice.

3. Results
3.1 Summary of tweets
The collected total of 109 tweets were categorized into the five categories according to the maincontents (Fig.2).
1) Flood inundation: tweets contain inundation-related information.
2) Rescue: tweets related to rescue or relief information, such as calling for help, seeking orproviding information to an evacuation shelter.
3) Emotion: tweets with personal emotion such as anxiety or fear.
4) River condition: tweets with contents about a river, water levels etc.
5) Damage situation: tweets have information on infrastructure, transportation, damage to daily
life etc.
Some tweets are related to more than a single category since the flood situations are
tweeted with the expression of fear. In that case, we manually judge the main content of each
tweet. If it is related to inundation or damage, we prioritize it because our research is interested in
the contents.
The largest category was about the flood inundation (37%), followed by the damage
situation (19%). The third largest category was on the river condition (16%). The portion of
tweets with some rescue-related information was 12% and 1% contained emotion. The othercategory (15%) is, for example, landslide information. Table 1 gives examples of each category.

15%

Inundation information
37%

Rescue information
Emotion

19%

River condition
Damage situation
16%

Others12%
1%Figure2 Categories and their proportions of the 109 tweet

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Category
Inundations

RescueEmotionRiver
condition
Disastersituation
Other

Table1 Example of tweets of each category
Example Morishita Street was inundated by the Hakkenbori floodgate; thewater depth was 1 meter My house was flooded, with water under the bed Our farm was inundated
 Help me, only my mom and I remain in our house. How can I
survive this night? # rescue # Kinu River # diffusion My parents are living in the town of Nakatsuma in the city of Joso.
The water depth increased after sunset, and now it seems that thehouse has been flooded, with water under the floor. What should I do
to prevent the water from rising? Who should I contact if something
happens at night? # Joso City # Kinu River #Flood' A street 10 kilometers from my house was reported to have suffered
flood damage. I got this information from Twitter and the news, but I
still don’t have a real feeling on it.
 This is the water level situation at Akutsu Bridge (with a photo), it
has been a long time since I have seen the water level this high National Route 16 was inundated
 The Joso Line between Moriya and Shimotsuma Stations was
flooded The water level rose again. Please be careful of landslides.
 A levee breach happened at Kinu River, and an alert for landslides
was also announced

We also analyzed the other general characteristics of the 109 tweets. Figure 3 shows that
43% of them were posted with photos during the flood. Half of them were related to the senders,
while the other 25% tweets related to other people. We could not link the remaining 25% witheither the senders or others. In addition, the time intervals between when the event happened andwhen people posted the tweets are important as disaster information. We compared the time
indicated in the tweets’ contents or the pictures with the posted time. If the time that related to thefacts in the tweets was the same as the post time in several hours, we defined it as a real- timepost. For instance, the tweet contents in ‘this is the inundation situation in front of the post officenow.’ The example of a non-real-time post is ‘I felt so surprised when I heard about the flood atlunchtime,’ but this post was posted in the evening. Other tweets have no clear evidence related totime, so we categorize them as the unknown. As a result, 32% of the posted tweets were real-time
posts, while 15% of them were about the non-real-time post, and 53% were unspecified due tothe lack of information indicating the time of events.

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Y.SHI, T.SAYAMA, K.TAKARA, K.OHTAKE

Tweets with photo

43%

Tweets related to themselves

50%

57%

0%

yes
25%

25%

no
unspecified

32%

Real-time (Near real time)

15%

0%

53%

50%

100%

Figure3 General characteristics of the collected tweets

3.2 Inundation information
We further investigated 37% of all 109 tweets (i.e. 41 tweets) categorized as “flood inundation.”
Table 2 shows the ratios of tweets with or without flood depth and location information. It
suggestes that 23% of the flood inundation-related tweets (i.e. out of 41 tweets) were able toestimate both the inundation depth and the location information based on the location name orlandmark information shown in the contents of tweets in the pictures. Based on the information,
we identified the location and water depth information. With the other 23% it was possible toestimate the tweeted locations but not the inundation depth.
Table2 Characteristics of tweets related to inundation

Geo-tagged

Non-geo-tagged

With depth-related information

23%

17%

Without depth-related information

23%

38%

3.3 Comparison with other inundation information
We compared the geo-tagged inundation-related tweets with the real circumstances of the Kinu
River flood disaster. The geo-tagged inundation-related tweets mean tweets with a clear landmark
or street name together with inundation-related information in the contents.

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Some examples of the extracted information are:1) What happened in Apita Supermarket and on Ishige Street?
2) Apita supermarket suffered severe damage. It looks like this.
3) Ibaraki Nursing Home was inundated; everyone waited on the second floor to be rescued.
4) Mitsuma Train Station was inundated.
5) Nakatsuma town was inundated with water rising from under the floor.
6) This is the current situation near Joso City Hall.
7) Ainomiya Street was flooded. My neighbor, who come from Brazil, is calling for help.
8) Morishita Street was inundated by the Hakkenbori floodgate; the water depth was 1 meter.
Figure 4 shows a comparison between the extent of the flooding and the information
posted in tweets. In this figure, the red line delineates the extent of the inundation at 18:00 on
September 10, 2015 (provided by GSI), while the color represents the maximum inundation
depths estimated by Sayama and Takara (2016). The figure shows that the content of tweets
generally agrees with the estimated areas of flooding. Referring to the flood extent measured by
GSI which is shown in the figure with the red circle, tweets No. 1,2 and 3 in Fig. 4 are welllocated inside the red circles in time and space scales. Moreover, the pictures from Apita
Supermarket, taken at 11:10 and 18:59 indicate nearly the same water depths, but showcompletely different amounts of drift deposits according to the pictures of tweets posted at
different timings.
Tweets 4 and 5 (in Fig.4) show the timing of water flowing and the expansion of the floodinundated areas. Tweet 4 was posted 37 minutes after the GSI had measured the extent of theflood zone at 18.00, and 3 hours later, tweet 5 reported an inundation located downstream. Tweets
6, 7, and 8 (in Fig. 4) show some differences in terms of the degree of flooding as measured by
the GSI, as shown in the red circle. Tweets 6, 7 and 8 reported severe inundation at 17:00, 22:21
and 22:38, which were not estimated by GSI. Following the crisis, it was confirmed that theseareas had been flooded.
Figure 5 shows details on flooding from the early morning in the city of Yuuki, upstreamof Joso. Based on the tweets, we can see that the region was severely flooded at 7:50. And theserver inundation-related information can give us an early warning on what might happen in thedownstream areas.

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Y.SHI, T.SAYAMA, K.TAKARA, K.OHTAKE

Figure4 Inundation information from tweets

The tweets shown in Fig. 5 reported the following information:
A: ‘Kinu Commercial High School was inundated.’
B: ‘The area in front of Kubota post office was inundated and people cannot pass National
Route 15 due to flooding.’
C: ‘This is the current situation of Sakae Bridge between the cities of Chikusei and Yuuki.’Previous studies indicated that social media users geographically closer to a disaster zone
have a higher intention of sharing more accurate knowledge (Thomson and Ito 2012).
Furthermore, our study shows many instances of clear data provided by residents inside the crisis
area. However, no tweets were posted near the levee breach points. This is possibly due to thelocation near the levee being one of the most severely damaged zones and residents may not have
had time to post anything.

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Figure5 Inundation information in the city of Yuuki

4. Discussion
Regarding the reliability of the tweets, several previous studies have tried to categorize Twitterusers to investigate the relationship between the reliability of tweets and the categories of users.
Li et al. (2013) indicated that well-educated people post more information that is reliable during
disasters with geo-tagging and photographs. Meanwhile, tweets posted by volunteers are also
considered as highly reliable information sources. Schnebele and Cervone (2013) and their seriesof studies (Schnebele et al. 2014; Schnebele et al. 2015) developed a method of crisis mapping by
combining remote sensing with local information sent by volunteers. They concluded thatvolunteered information can be a trustworthy source. In this study, we could not divide the posted
tweets by users’ categories or collect information exclusively from volunteers. Hence, all thecollected tweets in this study are posted from the public.
Another factor is on the contents of tweets in this research, Vieweg et al. (2010)
investigated how tweets may contribute to increasing the awareness of hazard events based on
two case studies. One event is the Oklahoma grass fire, which was an urgent disaster without any
alert in advance. The other event is the Red River floods, which was a disaster with lots of alerts
and evacuation preparations. The result demonstrated that in a more urgent condition like thegrass fire in Oklahoma, tweets were more related to evacuation information, damage and injury
situations, and hazard conditions including the wind direction of the fire line. In contrast, for mild
flooding with advanced warning, tweets were more related to preparatory activities, flood level,
9Y.SHI, T.SAYAMA, K.TAKARA, K.OHTAKE

weather and volunteer information (Vieweg et al. 2010). In our study, the Kinu River flood hasessentially both characteristics. With three days of heavy rainfall and its alert information, local
residents had a certain level of preparedness for the probable impending flooding. However, thelevee breach along the main Kinu River happened unexpectedly without any detailed alert interms of its timing and location. Our finding agrees with that of Vieweg et al. (2010) in the sensethat residents intend to report unexpected and sudden hazard situations as well as some damage
or relief related information because the situations of a disaster were different depending on thetime and area even from a single flood disaster.
From areas that were less affected, the number of tweets decreases. Xiao et al. (2015)
called these characteristics as a reverse U-shaped relationship between the distance for the most
severely affected disaster area and the number of posted tweets related to the event. This study
also agrees with their findings as there was no information from the most severely affected area.
In addition, this study suggests that many tweets were posted to report their situations not only
from the directly inundated areas by the levee breaching but also from fairly distant areas withlocal inundation along roads. The hazard information from less severely affected areas may also
be useful if it is shared effectively through a system like DISAANA (Ohtake 2015) sinceconventional mass media tend to report only on the most severely affected areas as long as they
are accessible. Moreover, our result in Fig. 5 emphasizes that a grasp of information on an
upstream area may contribute to the evacuation activities in a downstream area.
Finally, not all the tweets are reliable. Here we discuss how to check the reliability ofsocial media information for each usage that was identified in the introduction. For the first usage,
disaster occurrence detection as big data, generally a rapid increase of keywords in a short timefrom a particular location is difficult to occur just by fake news, so it is the most promising usage
with a number of previous case studies. In the second usage, how to identify critical rescue
requests has been difficult. To eliminate the necessary information, some organizations propose
adding particular hashtags; however, this has not been so successful as more and more people use
social media during disasters (Sato 2017). The same issue remains for the third usage, which isdisaster identification even though the selection of the information is not as critical for rescuing.
The following approaches can complement the selection of a limited number of reliable items ofdisaster information.
1) Automatic categorization or elimination of tweets to detect only first-hand information (e.g.
Abbasi and Liu 2013).
2) Effective use of tweets with location information and photos. Our study suggestes that thesetweets describe realistic flood conditions. A system like DISAANA to automatically map
such tweets is helpful for grasping a global view on what is happening in an area.
3) Crosscheck with other information sources such as mass media or government information.
Multiple information about social media itself also helps to improve the reliability (e.g.
Takahashi and Igata 2012).
If we check the social media information with the above methods before further
utilization, the reliability of social media information may be improved.

5. Conclusion
We detected and analyzed 109 tweets posted from disaster-affected area by the flood in the Kinu
River Basin.
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We manually examined them to interpret their main messages. The greatest proportion(37%) contained details on flooding, followed by information on damage (19%). The third
highest number of users posted was about water level (16%). Rescue information was alsocollected (12%). In addition, emotions accounted for 1%, while other types of data comprised
15%. This demonstrates that many people intend to report on their local flood situation.
Regarding the timing of tweets, we identified 32% as being posted in or near real-time;
15% show clear evidence of not having been posted in real time, while the rest (54%) do notreveal whether they were posted in real time among the 46% tweets containing inundation-related
information, and 23% were posted with a clear indication of the flood zone and water depth.
From this 23%, in the future, residents could develop a very clear image of risky areas and canprovide early warnings to others. The other 23% contained exact site information but water depth
was uncertain; thus, the second 23% only provided residents with knowledge of places where
they should exert caution.
Comparing well-positioned inundation-related tweets with the degree of how far the flood
spread as estimated by GSI, the well-positioned inundation-related tweets show good agreementwith the estimated inundation extent at different timings. Furthermore, some tweets suggested
additional inundated sites, which were not originally identified by the GSI, but later confirmed byfield investigation.
The outcome shows that many people posted information during a disaster, and thewell-positioned first-hand tweets display reliability with the advantage of having wide coverage.
However, this study did not detect and analyze tweets automatically; the main messages,
location, and water depth were estimated manually. Further work on the automatic detection andanalysis of tweets is required. At the same time, as the available tweets with references oflocation and water depth are limited, the contribution of social media in flood mapping will be
improved if we can obtain multiple social media information sources in real time.

Acknowledgment
We would like to express our sincere thanks to the National Institute of Information andCommunication Technology (NICT) for providing the DISAster-ANAlyzer system. And the firstauthor acknowledges the Inter-Graduate Program for Sustainable Development and Survivable
Societies (GSS program) for supporting the research activities.

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