AI AND DISASTER
DISASTER SEEN IN WATSON

IBM Japan, Ltd.
Akiko Murakami
Masayasu Muraoka
Hideyuki Mizuta
Kaori Namba© 2017 IBM Corporation

AGENDAAn actualization of operational risks using text
analytics approach in Fukushima power plant
disaster
Akiko Murakami,Tokyo Software Development Laboratory

Use case introduction:Twitter analysis towards
indirect support for Kumamoto Earthquake
Masayasu Muraoka, IBM Research AI

Large-scale agent-based social simulation for traffic
and pedestrian in a city
Hideyuki Mizuta, IBM Research AI

AN ACTUALIZATION OF OPERATIONAL RISKSUSING TEXT ANALYTICS APPROACHIN FUKUSHIMA POWER PLANT DISASTER
2017-11-28

Akiko Murakami, Kaori Namba, Teruki Tauchi, Hiroya Matsubara (IBM Corp.)
Akio Suzuki (NHK: Japan Broadcasting Corporation)
Michinori Hatayama (Kyoto University)1© 2017 IBM Corporation

LEADERSHIP AT THE DISASTER TIMES• String leadership is needed at the disaster time
• However, it contains some risks: Health concernof the leader and the missing ideas from others• Analyzing the documents (Reports, transcript ofdecision making) at the disaster time might beuseful for the future disasters

2

OVERVIEW OF THE FUKUSHIMA DAIICHINUCLEAR POWER PLANT ACCIDENT• Initiated primarily by the tsunami following theTohoku Great Earthquake on 11 March 2011
• The accidents occurred by insufficient cooling
2011
March 11hydrogen-air
explosions

Accidents
firenuclear meltdowns?

release ofradioactive
materialActions
evacuation

3drain water offfor coolingRESPONSE TEAM FOR THE ACCIDENT• The owner of the plant, Tokyo
Electric Power Company (TEPCO),
had three teams for the disaster
response

KashiwazakiKariwa
NuclearPower Plant• Headquarter in Tokyo
Fukushima
NuclearPower Plant(Daiichi, Daini)Tokyo ElectricPower Co.
Headquarter4• Fukushima (Daiichi and Daini) andKashiwazaki-Kariwa Nuclear PowerPlants• The plants and HQ are locateddifferent locations, several
hundreds kilometers away

VIDEO CONFERENCE SYSTEMS FOR TEAMCOMMUNICATIONS• TEPCO used a video conference
system for teams’ communication• After March 15, the government
decided that consolidated teamincluding TEPCO, Government
and Nuclear and Industrial Safety
Agency use the system forcommunication
http://photo.tepco.co.jp/date/2013/201303-j/130029-01j.html •5Almost all recorded video areavailable in publicCan we utilize the data for future disasters?

TRANSCRIPTION DATA OF THE ACCIDENT• Dictation by human, with any filler words
and/or hesitations• One speech by one person is recognized
as one document• Each speech (document) has metadata:time and day, speech location, speaker,
speech contents (text)
6

EXAMPLE OF THE DATA
本店
武藤副社長
えっと、吉田所長。
１Ｆ
吉田所長
はい、吉田です。
本店
武藤副社長
機動隊の消防隊がオペレーターも含めて小名浜でえー準備が出来ていて、これから、あーいつでもえーそちらに行って頂けるというこHQ
Muto SVP
Hello, Director Yoshida.とになりました。
1F
Director Yoshida Yes sir, this is Yoshida.
１Ｆ
吉田所長
HQ
Muto はい。
SVPFire prevention team in the mobile unit
本店
武藤副社長
それで、がれきの除去がいつできるのか、現場に何including operators
in Onahama, now are ready, well, yea, they can go your
時頃行けるような条件ができるのかというのを確認をしてその時間に入れるように
site anytime.
準備をして頂くということになるので、がれきの除去にかかる時間を評価をして教1F
Director Yoshida Yes, sir.
えて下さい。 HQ
Muto SVP
Then, we will confirm that when we can do
１Ｆ
吉田所長
remove heaps ofはい。ご要求は理解しましたが、ちょっと一つ確認さrubble, when the condition is ready to go the disaster place,
せて下さい。がれきの除去と言っていることに関しまして２点、今ございましてですand would like to ask you to prepare for it, please estimate the time for
ね、あの、今４号機の山、山側の道のがれきの除去のことでよろしいですか？
removing the rubbles.
本店
武藤副社長
４号機のプールに対して放水できる所までたどり着け
1F
Director
Yoshida Yes, I understand your request, would you mind
る為のガラスの…がれきの除去っていうのは、山側が近いのか、海側が近いのか
if I ask you two question? Did you mean the place is that heaps of rubble inによりますけども。
the side of Unit4, align to the mountain side road?
HQ
Muto SVP
I meant that the way to Unit4’s pool with glass
rubbles – removing rubbles, it depends on the situation, mountain or sea side.
7

DATA DETAILS≒7500• One speech by one person is recognized as onedocument• 34,432 documents, from March 12th to March 31st• There is no data for March 15th3/1283/31

WATSON EXPLORER: A TEXT MINING
TOOL WITH COGNITIVE TECHNOLOGIES• Watson Explorer is a tool to do text analysis
and mining interactively• Powerful Text Analysis features with richvisualization• Flexible application customization
capability with True WYSIWYG editor

9

TEXT ANALYTICS“MINING INSIGHTS FROM TEXT”
StructuredData• Semantics of information
is explicitly defined by thestructure of data
• e.g. Tables in RDB• Text mining is a technology
combining structured and• Semantics of information
unstructured data andis hidden in character (orshowing their statistics withbinary) streams• e.g. Text, Graphics, Voicegraphical interface to findout “insight”
UnstructuredData• Information extraction
technology enables us toextract information as… structured data from textual
document using Natural
Language Technologies
Graphical Interface
10

WHAT WE UNDERSTAND FROM THE DATA
1. Visualize the Rotation of theleadership
2. The risk of keeping leadership bysingle person
3. Visualize the Conversation Ratio
Between Organizations

11WHAT WE UNDERSTAND FROM THE DATA:LEADERS ROTATION OF THE TEAMS
1400

Speech frequency par day

1200
1000

Yoshida-D

Takeguro-F

Komori-VP

Muto-SVP

800
600
400
2000
12

Director Yoshida
Komori VP
Muto SVP
Takeguro Fellow
1213

1415

1617181920

21222324

25

2627

28

2930

31

Fukushima
Leadersin HQ

Fukushima team was led by the single leader

02011/3/12 22:00
2011/3/13 7:00
2011/3/13 16:00
2011/3/14 1:00
2011/3/14 10:00
2011/3/14 19:00
2011/3/15 4:00
2011/3/15 13:00
2011/3/15 22:00
2011/3/16 7:00
2011/3/16 16:00
2011/3/17 1:00
2011/3/17 10:00
2011/3/17 19:00
2011/3/18 4:00
2011/3/18 13:00
2011/3/18 22:00
2011/3/19 7:00
2011/3/19 16:00
2011/3/20 1:00
2011/3/20 10:00
2011/3/20 19:00
2011/3/21 4:00
2011/3/21 13:00
2011/3/21 22:00
2011/3/22 7:00
2011/3/22 16:00
2011/3/23 1:00
2011/3/23 10:00
2011/3/23 19:00
2011/3/24 4:00
2011/3/24 13:00
2011/3/24 22:00
2011/3/25 7:00
2011/3/25 16:00
2011/3/26 1:00
2011/3/26 10:00
2011/3/26 19:00
2011/3/27 4:00
2011/3/27 13:00
2011/3/27 22:00
2011/3/28 7:00
2011/3/28 16:00
2011/3/29 1:00
2011/3/29 10:00
2011/3/29 19:00
2011/3/30 4:00
2011/3/30 13:00
2011/3/30 22:00
2011/3/31 7:00
2011/3/31 16:00

WHAT WE UNDERSTAND FROM THE DATA:DIR.YOSHIDA KEPT HIS LEADERSHIP
Speech Frequency of Yoshida Director / Ratio of the speech

120

60

100

80

No
data

60

40

20

13
Sickleave
He wasin Gov.
to report
theaccident• Director Yoshida did not sleep well (3-4 hours par day)• Except reporting the accident to the government, he kept his leadershipthough the accident• He left his leadership for few hours because of his sickness

Can we predict this risk based on the data?

50

4030

20

100

STAMMER EXPRESSIONS IN THE SPEECHREPRESENT THE PERSON’S TIREDNESS
あの、まあ、そうですね。
はい、はい、そうです。
やら、やらざるを得ないんだから。

• This is a record of conversations,
not a formal speech, so it contains
some filler, stammer and some
expressions with hesitations

消防、消防団はまだいないの。

• FillerWell, yaa, I got it.

• Repeating same words/phrases

Yea, yea, that’s right.
We have to do, do.
Do we have fire, firemen?
14• Extracting these kinds ofexpressions automatically using NLP
technologies

TIME SERIES OF REPEATING EXPRESSIONSIN DIRECTOR YOSHIDA’S SPEECH• The frequency and the ratio of repeating expressions in thespeech for Director Yoshida are slightly higher than others’
TIME SERIES OF REPEATING EXPRESSIONSFREQUENCY & TREND VALUE
120

108
6
4
20-2-4100
80Freqency

Trend Value

60
4020012131415

1617181920

21

2223

24

25

2627

28

2930

31

The day of the sick leave• Statistics shows that the expressions increase irregularly at theday of his sick leave
15

DISCUSSION:SINGLE-LEADERSHIP VS. MULTI-LEADERSHIP• The Fukushima team had a risk for loosing theirleader• The text analytics result shows that he had sometrouble with physical or mental health• Why was the Fukushima team led by the single
leader?
• At the first stage of the disaster,TEPCO considered thatFukushima team should have multiple leaders• They had several sub-leaders, but the leadership ofDirector Yoshida is very strong so that the team reliedon his leadership
16WHAT WE UNDERSTAND FROM THE DATA:THE RATIO BY ORGANIZATIONRatio

2F
1%

KK
1%

JV
3%
OSC
5%

45%
HQ

HQ

Fukushima 1

J Village

Fukushima 2

Other

17

• Assumption:
Kashiwazaki-Kariwa andFukushima-daini did not
perform their
Offsite Center
knowledge effectively
Kashiwazaki

45%

1F
43%
43%

• Most of theconversation occupied
by HQ and 1F(Fukushima-daiichi)

WHAT WE UNDERSTAND FROM THE DATA:NEXT SPEAKER OF DIR.YOSHIDA
Location

2F
1%

KK
1%

JV
2%
OSC
5%

1F
29%

HQ

Fukushima 1

J VillageFukushima 2

Other18

HQ62%• After Dir.Yoshida’sreports, HQ reacted
these reports and makedecisions• Sometimes other person in 1F
reported continuously• Kashiwazaki-Kariwa andFukushima-daini did notreact to Dir.Yoshida’s
Offsite Center
Kashiwazaki
reports

WHO TALKED AFTER KASHIWAZAKIKARIWA?
Location

2F
3%

KK
3%

OSC
6%

1F
25%

19

HQ
62%

HQ

Fukushima 1

Offsite Center

Kashiwazaki

Fukushima 2

Other• After Kashiwazakikariwa’s speech, HQ
reacted to them first• 1F and KK and 2F
did not
communicate
directly – allconversation though
HQ

DISCUSSION: HOW TO UTILIZE THE DATA
AND ANALYTICS RESULT• How can we get the data?
• This time the disaster area and the headquarters
are located in different places so that almost all
conversation are stored as recoded video• However recognizing the conversation in real time
is difficult due to the sound quality, etc.

• How can we recognize the types of risk?
• We observed several types of risks in the data
With analytics technology we can retrospect
the disaster and the operation for it20

CONCLUSION• We show the text analytics results with theconversational data at the disaster time
• The results show the risks of operation:
single leadership and the lack of leadership• From these experiences, we might be able tolearn how to better organize and leaddisaster response teams

21

END OF THE PRESENTATION