ISSN 2186-7437

NII Shonan Meeting Report

No. 2014-8

Water Disaster Management and Big
DataFang-Pang Lin
Satoshi Sekiguchi
Philip Papadopoulos
July 7–10, 2014

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Water Disaster Management
and Big Data
Organizers:
Fang-Pang Lin (NCHC,NARL, Taiwan)
Satoshi Sekiguchi(AIST, Japan)
Philip Papadopoulos (UCSD, USA)
July 7–10, 2014
Water disasters include flooding from typhoon, seasonal storms and othernatural or man-made causes, usually coupling with land, mud-slides and often
occur without warning. Water disaster management is a challenge that faces
many parts of the world. Big data emerges as a new opportunity for prediction
and decision support. Using water disaster management as a specific disaster management application, the goal of this meeting is to bring together aninterdisciplinary community of researchers, practitioners, and developers to explore current and future challenges identified by planners and responders that
require research, development or deployment of big data and cyberinfrastructure solutions. This Shonan meeting makes use of the unique opportunity tobridge various areas and to establish sustainable research collaboration between
various communities. The seminar comprises both representative backgroundpresentations to set the context for discussions, working sessions to develop joint
research agendas, and sessions that focus on joint problem-solving of a target
issue selected during the meeting. The seminar report includes the framework of
the discussions that leads to new ideas and summaries of presentations following
the breakout sessions.

Background
Over the 12 years of PRAGMA (2002 2014, member institutions had experienced several types of disasters: typhoon damages (annually in Taiwan in particular Nock-ten October 2004, Morakot August 2009), flooding (Thailand 2011,
Australia), earthquake/tsunami (March 11, 2011 tsunami that affected Japan),and fires (October 2003 and 2007 affecting San Diego). At the PRAGMA 21
Workshop, sponsored by GEO Grid supported by MEXT Japan, organized
by AIST, co-organized by Osaka U, Titech, and U Tsukuba, with many othersponsors(see http://pragma21.pragma-grid.net/dct/page/1) there was a special
session on GEO Grid and a focus on this in the PRAGMA 21 Workshop. In
discussions, the keynote speaker Kenzo Hiroki (ICHARM) was asked the question• If you could have (or improve) one technology in handling a disaster, what
would that be?
1• His response was: Have a technology where he could have all of the (distributed) people responding to focus, simultaneously, on the same pieceof information (e.g., video or data feed).
• His presentation and the ensuing discussion stayed with the participants
for many years.
Given the nature of PRAGMA, to delve more deeply into how to address
this concern (and other concerns around disaster management), we knew weneeded to have an extended, dedicated time for discussions between disastermanagement and IT experts. The Shonan venue gave us that opportunity. A
proposal to Shonan was first discussed at Supercomputing November 2012 (Salt
Lake City). It was decided then to focus on water disaster management, bothto allow us to make progress, but also to reflect the issues that motivated theorganizers. A proposal was finally submitted in 2013, and accepted in December
2013.
In parallel to PRAGMA’s journey to this meeting, the larger community,
within countries and internationally, saw needs to address issues around the entire disaster management cycle. One most relevant to this workshop is National
Research Council. Improving Disaster Management: The Role of IT in Mitigation, Preparedness, Response, and Recovery. Washington, DC: The National
Academies Press, 2007.

Topic of Interests
Water disasters include flooding from typhoon, seasonal storms and other natural or man-made causes, usually coupling with land, mud-slides and often
occur without warning. Water disaster management is a challenge that faces
many parts of the world. Big data emerges as a new opportunity for prediction
and decision support. Using water disaster management as a specific disastermanagement application, the goal of this meeting is to bring together an interdisciplinary community of researchers, practitioners, and developers to explore
current and future challenges identified by planners and responders that require research, development or deployment of big data and cyberinfrastructure
solutions.
The topics will include:• Water disaster events and mitigation practices• High frequency sensor networks and fast response.
• Remote sensing and aerial imaging• Flood and land sliding modeling• Crowd sourcing and data mining for water disaster management.
• Tera- and Peta- scale data infrastructure• Configurable and high availability networking

2Participants
Title
Dr.
Prof.
Dr.
Dr.
Prof.

First Name
Fang-Pang
Phil
Satoshi
Junichi
Lan-KunLast Name
Lin
Papadopoulos
Sekiguchi
Aoki
Chung

Prof.
Prof.
Dr.
Prof.
Dr.
Dr.
Dr.
Mr.
Dr.
Prof.
Dr.
Dr.
Mr.

Shinji
SusumuPeter
Jose
Whey-Fone
Prapaporn
Sirod
Bo
Kyoungsook
Yoshiyuki
Jason
HiroakiChen-Yu

ShimojoDate
Arzberger
FortesTsai
Rattanatamrong
Sirisup
Chen
Kim
Kido
Haga
Yamanaka
HaoProf. Wanida
Dr. Yasuhiro

Affiliation
NCHC, NARL (TW)UCSD, San Diego (US)AIST (JP)
Hitachi, Ltd. (JP)GIS Research Center, Feng Chia
University (TW)Osaka University (JP)Osaka University (JP)NSF (US)
University of Florida (US)
NCHC, NARL (TW)Thammasat University (TH)NECTEC (TH)NSPO, NARL (TW)AIST (JP)Osaka University (JP)AIST (JP)NICT (JP)GIS Research Center, Feng Chia
University (TW)Thammasat University (TH)NICT / ICSU-World Data System (JP)Putthividhya
Murayama3

Program
Structure of the Workshop:
We anticipate a series of presentations, increasingly technical, to provide examples of the needs of individuals and groups responsible for anticipating, planning for, and responding to natural disasters caused by water. We plan to haveindividuals from several countries present scenarios where there are technicalchallenges. We anticipate that there will be a dialog between individuals andgroups planning or responding to aspects of disasters and individuals who canunderstand and ask questions to clarify technical aspects of the challenges. Outof that discussion we hope to classify challenges as long term (research: there
are basic questions for which there are no existing solutions), medium term (development: most of the technologies are developed but need to be integrated ortested or modified) and short term (deployment: (affordable) solutions exist).

Goals of Workshop:
The workshop will produce a report for the community. We plan to achieve thefollowing goals in water disaster management and big data:
1. generate possible research opportunities for funding agencies (such as
JSPS or NSF);
2. create some personnel connections between participants;
3. develop funding proposals to further ideas from the workshop; and4. in the PRAGMA Context establish a working group or new expedition to
prototype solutions, in collaboration with groups with the needs.

4Day 1: Scenarios andfs Problems in Water Disaster Management
7,July,2014 Activity
Chair
9:00-9:10
Introduction of the Meeting: Morale & Objec- Satoshi Sekiguchi,
tives
Peter
Arzberger,
Phil Papadopoulos,
Shinji
Shimojo,Fang-Pang Lin
9:10-9:30
Whoś who
Fang-Pang Lin
9:30-10:30 Water Disaster Management Practices & ex- Whey-Fone Tsai
periences Sharing (I)
10:30-10:50 Coffee break
10:50-12:00 Water Disaster Management Practices & ex- Bo Chen
periences Sharing (II)
12:00-13:00 Lunch
13:00-14:30 Scenarios Build Up & Problems Identification Peter
Arzberger,
Phil Papadopoulos
14:30-14:50 Coffee break
14:50-17:00 Break-up Discussions in Identification of Key Kyoungsook Kim,
Issues (3 working groups and working on Jason Haga, Prapaa framework with quick and slow onset porn Rattanatamdisaster types and operations on monitor- rong
ing/predicting, communicating and responding, as the starting point)

Day 2: Identify Key Topics
8,July,2014 Activity
Chair
9:00-9:10
Wrap-up Report from Day 1
Fang-Pang Lin
9:10-10:30 Big Data: Challenge and Opportunities Shinji Shimojo, YaBreak-up Discussions in Identification of Key suhiro Murayama
Issues and Opportunities
10:30-10:50 Coffee break
10:50-12:00 Presentation on Identified key Topics from Lan-Kun Chung
Each Working Group
12:00-13:00 Lunch
13:00-14:30 Searching for Solutions: Sharing of Experi- Wanida Putthividences & Technology from Participants (I)hya
14:30-14:50 Coffee break
14:50-17:00 Searching for Solutions: Sharing of Experi- Jose Forte, Fangences & Technology from Participants (II)Pang Lin5

Day 3: Technologies and Solutions
9,July,2014 Activity
Chair
9:00-9:10
Wrap-up Report from Day 2
Fang-Pang Lin
9:10-10:30 Breakup Discussions in Technology and Solu- Fang-Pang Lin
tions (I)10:30-10:50 Coffee break
10:50-12:00 Breakup Discussions in Technology and Solu- Lan-Kun Chung,tions (II)SusumuDate,
Sirod
Sirisup,Janyu How
12:00-13:00 Lunch
13:00-17:00 Excursion in Shonan Area

Day 4: Summary
10,July,2014 Activity
Chair
9:00-10:30 Overview and Guidelines
Peter Arzberger
9:10-10:30 Report from Technology & Solutions Working Fang-Pang Lin
Groups
10:30-10:50 Coffee break
10:50-12:00 Future Development & Action Items
Fang-Pang Lin
12:00-13:00 Lunch

Key Observations
Key observations from the meeting (some of these were stated at the meeting,others are based on reflection of meeting discussions)• Disasters are not confined to national boundaries. Learning how to effectively manage them is an opportunity to share effective practices andapproaches in an international setting• Disaster management (including all four phases see below) is an extremelycomplex operation, requiring collaborations among many expertise andultimately among many agencies.
• Ability to access and process data, often real time, is critical to improve
managing disasters.
• There are major opportunities for and need of information technology
most phases of disaster management. While the NRC report cited above
covers many of these challenges, technology has continued to evolve. One
technology that offers opportunities and challenges is social media. The
opportunity is the ability to connect with people and expand the sensing
and real time data into managing disasters (both data in and data out).
On the other hand, there is a great deal to learn about validating suchinformation and how all participants should understand these data.
– A statement from one of the references indicated that in none disaster scenarios, much effort is put into ensuring technologies and6approaches are efficient; however in the cases of disasters we needsolutions that are scalable and resilient– Currently, most academic resource providers do not think/test aboutbacking up services (like they have learned to back up data)• There are key assumptions that underpin many scenarios: namely that
there is communications and power.
• In thinking about responding to disasters, one needs to define success.
This could be saving lives or saving some particular lives, protecting property,• While we did not dwell on this, there are potentially disruptive technologies / disruption deployments in the future– Ubiquitous communications– UAVs for data gathering, available for rapid deployment– Tiling tablets for shared visualization
Phases in Disaster Management

From Earthzine(http://www.earthzine.org/disaster-management-theme/).
Vanneuville et all, posted March 21, 2011(http://www.earthzine.org/author/Vanneuville/)in Is Flood Risk Management Identical to Flood Disaster Management?

Figure 1: Phases in disaster management according to Vanneuville et al.

Summary of Discussions
The Scenarios: The group focused early in the meeting on three motivatingtype of disasters:7• Flooding: This can result from typhoons as well as ongoing and frequentmonsoons. The image many participants had was the flooding in Thailand
in 2011.
• Tsunami: The image participants had was of the devastating Tsunamithat struck Japan in March 2011. In addition there was a severe Tsunamithat hit Aceh Indonesia in 2004.
• Typhoon: These affect the region, from the Philippines (most recently
November 2013 with Typhoon Haiyan) to Typhoon Morakot (August
2009;
http://en.wikipedia.org/wiki/Typhoon_Morakot.

) that created conditions that created a mudslide that buried a village),to others that affect Japan, Korea and China.
There are commonalities among the scenarios• Need to integrate data from various sources• Promise and challenges in social media, in getting information to and from
population, and being able to have a measure of validity about data fromothers• Predictions of impact of disaster are required for allocation of resources
for response and recovery. Often this will require models of the natural
disaster (e.g., trajectories of typhoon, predictions of rainfall).
In addition, there are differences, chief is the time scale in responding to adisaster, with the shortest possibly being tsunami, to the longest being floods.
However, many factors are still difficult to predict, e.g. exact amount andlocation of rainfall.
Addressing Scenarios: Different Approaches, Different User Needs
Figure 2 (using iMindMap software to visualize information) resulted fromthe first breakout group session that used the three scenarios, but looked at
different approaches:• Monitoring• Predicting• Operations/Infrastructure
In each of these scenarios there were user perspectives. But even here there
were different type of users• Response Managers (needing to decide where to allocation resources)• First responders• Data contributors (acting as sensors for disaster response systems)

8Figure 2: The mind map from the breakout session of the day one.
• Victims (how to get help)
This is not an exhaustive list. In some cases, there were very specific suggestions for testing pieces of approaches to determine how difficult it would be
to implement some of the technologies. In particular see diagram for testing the
migration of a data center.
One common theme among the approaches is data: Generating, accessing,using, and communicating.
Specific Scenarios: What If
To try to be more concrete about what would we focus on as subsets, after the
meeting concluded, we formed three breakout groups to identify a specific scenarios problem, what if we could have or know something, and what technology
might we want to use. The three scenarios are:• Typhoon Attack: Rainfall– We would like better prediction of rainfall, both amounts and locations. This would allow us to better predict location of flooding,debris flow, and chances of mudslides– Monitoring Technology that we would use:
∗ Remote sensors (Radar, UAV)∗ In-Situ (Ground based) increased coverage cameras, sensors∗ Uncontrolled sources (social media, mobile sensors)– Infrastructure (same order as monitor) we would want to have
∗ Remote sensors: international connections networks (politics)∗ In-situ: redundancy Network, optimization, placement∗ Intelligence systems/filtering, virtualization solutions
9

– Prediction Models (this was not discussed explicitly) but one would
need both better (pre-calculated) models of where there are mudslide
hazards, and better run-off models.
• Typhoon Attack: Communicating– When typhoon is coming, authority want to know the location andinformation of people who still struggle in the mountain area, or
during disaster, the location of isolated peple– Assumption: Power and communication still partially work
– Solution: Infrastructure: SDN, community network– Prediction: potential dangerous areas (landslide, debris flow)– Monitoring: not finish yet• Flooding– Flooding slowly approaches to capital city
∗ Where to divert water, to airport or city?
∗ What information is required for decision makers– What if we can have
∗ Virtual environment for decision making∗ Map interface sharable among decision makers– Where is hospital, airport, and power plant∗ Superimpose (overlap) 4D Geo, time-referenced data
∗ Decision maker must want to know the situation over time
– Technology∗ Tile display wall, tiled tablet
Concrete Steps:

What happens after meeting?

• There are several ways for the community to continue to develop theseideas and to try out some of the ideas. Two particular venues are– PRAGMA Workshops, with the next two planned for∗ 2014 October 15 17, Bloomington Indiana USA∗ 2015 April 8 10, Nara, Japan– Southeast Asia Institute program (SEAIP),∗ 2014 December 1-5, City TBD, Taiwan• Specific Activities to be tried– Migrate a Data center, using specific remote sensing data (phasedarray radar)– Build collaborations between Taiwan and Japan for sharing phasedarray radar data (that can give more information about amount andlocation of rainfall during a typhoon)– Other10Figure 3: Migration of data center during water disasters.

Figure 4: Migration of data center during water disasters with a big data framework, plotted in red.

Meeting Structure and Participant Composition
There were 22 attendees of the meeting. The meeting schedule and structure
allowed for a great deal interaction. Notable about the interactions:• The small number of participants allowed for people to know everyone
at the meeting. It was helpful that nearly half of the participants are
regularly engaged in PRAGMA, and many others were known to at least
one of the PRAGMA members.
• There were different expertise present on the technology side, and a few
people who had closer experience with disaster management.
• Two entire walls of white boards allowed for much real-time sharing ofideas about specific topics and sketching out ideas• Breakout sessions (two sessions, different groups and topics) allowed forsmaller groups to meet
• Critically, many junior members were asked to chair the breakout sessions,giving them an opportunity to shape discussions.

11Comment for future meetings:• We need to do more to attract women participants. At this meeting only
three participants of the 22 where women.
• Also, we need to plan to have more people who have been directly involved
with disaster management at a meeting.

References[1] National Research Council. Improving Disaster Management: The Role of
IT in Mitigation, Preparedness, Response, and Recovery. Washington, DC:
The National Academies Press, 2007.
[2] Veronica Grasso, et al. A UNEP Report: Early Warning Systems: A Stateof Art Analysis and Future Directions. United Nations Environment Programme, Nairobi, 2012.
[3] Japan International Cooperation Agency. Water Resources and Disaster
Management.
http://www.jica.go.jp/english/our_work/thematic_issues/water/[4] Sergio Freire, Biswajeet Pradhan and Christoph Aubrecht. Disaster management Theme.
http://www.earthzine.org/disaster-management-theme/[5] ICHARM. International Centre for Water Hazard and Risk Management.
http://www.icharm.pwri.go.jp/about/about.html[6] Shigeru Morichi et al. Disaster Management Policy Program.
http://www.grips.ac.jp/cms/wp-content/uploads/
2012/05/dmp_pamphlet.pdf[7] UNSGAB. World Water Forum 5.
http://www.worldwatercouncil.org/fileadmin/wwc/
World_Water_Forum/WWF5/Water_and_Disaster.pdf[8] Mohd Nazshua bin Mohd Irwan Serigar. The Mirror of Ibnu Sina.
http://nazshua.blog.com/2010/12/10/emergencymanagement-preparedness-response-and-recovery/[9] David W. Cooke, et al. The Resilience of the Electric Power Delivery System in Response to Terrorism and Natural Disasters.Washington, DC: The
National Academies Press, 2013.

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Appendices
Comments during the presentations are shared through Google Docs as seen in
the appendices below. All meeting documents presented or developed at themeeting are shared through shared google drive for all participants.
Online discussions on Google Document:——————————————————————————————
Shonan Meeting (51): Water Disaster Management and Big Data
Scratch Note for ideas:
Format [your name]: your ideas——————————————Satoshi(S): FYI. ALOS-2 capture any area everyday with its emergency mode,
while 14days in nominal mode. Satoshi(S): More to know about FORMOSAT5. Sattelite Constellation is useful. Key issues: Networking groundstations,Standardize command to control these sattelites.
http://www.nspo.org.tw/2008e/projects/
project5/intro.htm
Shinji: Networking groundstations are very interesting. But Can It be temporalor sustainable? How much bandwidth we need ?
Satoshi(S): AFAIK unfortunately, it requires temporal but peaky bandwidth.
FP: HPWREN provides sustainable service to observatory transmitting images
between groundstations. Good for last mile that requires heavy loading of moving data. We need something like Bandwidth-on-Demand.
FP: Public involvement now plays a crucial role in Taiwan. The problem isthe data quality when you hand some responsibility to the public.
Jason Haga: Is it possible to use NPOs for some of the crowd-sourced data?
They could have trained volunteers and it might help increase the veracity ofdata. But the coverage may not be ideal.
FP: The purpose of involving public is not only for the coverage issues, in which
sensor network always has limitation, but also for avoiding direct impact to thepeople from disasters. People tend to respond themselves using their own way,since most methods government introduced are not working properly.
Jason Haga: One comment about Slayers Geothings. He deployed a mobile
app to be used by NPOs for reporting. The mobile app became more and more
simple with each new version, eventually ending up with a single button to push.

How: its interesting that utilized app to report flood level, for avoiding typo13

error, maybe we can use automated image recognition tech to determine thevalue from colum images.
Wanida(Tik): There is a project in Thailand (NECTEC) utilizing social network to report traffic condition. Good points are lots of data from all the places.
Down side is that they need to clean up and pre-process the data into the format
that can be used
Satoshi(S): My 2yens comment on Peters talk. It would be useful if the scale
sing with poles has an ID Q-code (or similar) to
Phil: Can we simulate an outage of a data center that holds satellite data
and reroute/reaccess via PRAGMA Experimental Network Testbed (ENT)? Is
there old satellite data we share among PRAGMA (and others!, but not theworld) for testing?
Whey-Fone: It is possible to test the case of satellite rapid image processing for emergency reponse to disaster event occurred in Japan and Taiwan, as
presented by NSPO Bo Chen, via Japan-Taiwan APAN network or Japan-USTaiwan network.
Peter A: At some point we may want to look at several reports, to see what
they have said. one report is Improving Disaster management, the role of IT in
Mitigration, Preparedness, Response and Recovery
http://www.nap.edu/download.php?record_id=11824
FP: A reference that I used for this meeting: A UNEP Report: Early Warning
Systems: A State of Art Analysis and Future Directions (2012):
http://na.unep.net/siouxfalls/publications/early_warning.pdf.
Whey-Fone: Stallite radar image data is very useful for disaster scenario analysis; while ground radar observing spatial vapor data is critical for forecast of
typhoon induced and storm rainfall, and hence, contributes to the disaster response.

Satoshi(S): Will Formosat-5 have SAR system ?

How: in some cases, typhoon causes flood.
Satoshi(S): Cause - Earthquake, Heavy rain, Typhoon -¿ Resulting- Flood, Debris flow, Tsunami
FP: Complexity in the causes.

14

Jason Haga: Common problem areas I see, but please add or correct if wrong
1) Data/information collection and dissemination (crowd/sensors)
2) Data filtering/fusion (operations on variety of data types)
3) Short vs. long term responses (either case must be fast) [clarification short/long are relative, minutes may be short, hours may be long, or perhaps
days are long; also, we discussed this both in terms of warnings about upcoming
event, and getting word back to community (PeterA)]4) Cultural background and education (by shinji)
5) Infrastructure to support real-time information

Peter A: Some initial comments from discussions: - Need to define successand/or KPIs. - Think about getting information in as well as informing individuals (getting information out)Peter A: General comments about three scenarios - Integation of data fromvarious sources, some of the data real-time- Getting information to community- There is a promise and challgenges (trustworthiness)of social media, both getting data into a control center, and getting it out (sincesocial media can a be an all-to-all network) - Prediction is required before distribution of resources- Modeling is required for prediction- There are assumptions that communication / electricity persist

FP: Big Data presented here by Murayama-san means getting data in finer
resolutions in both temporal and spatial scales. The mash-up google map withboth rainfalls observation and traffic and its implication for possible analysis/prediction on human behavior is interesting, which is something beyond the
traditional efforts focusing merely on resolutions.
PMP: Radar data is very interesting. Is there a way we could replicate some ofthis data to PRAGMA sites? Is the data format open so that others could workwith it? It seems that processing this data at other sites could make a use casefor disaster preparedness – in other words if processing pipeline is replicatedelsewhere, then in the event of local issues (power, for example, it might still bepossible to process the incoming data stream elsewhere)
Here is the image Fang-Pang Showed:
http://www.earthzine.org/wp-content/
uploads/2011/03/Figure-2cropped.jpg
Satoshi (S): Phils idea - Ad hoc Tiled Display (?) bring your own tablet toconfigure a large display connected. easy to attach and detach automaticallyreconfigurable.
Jason Haga: See these papers for ad-hoc mobile display
http://juliaschwarz.net/research/phone_pixel.pdf
15

http://www.graphics.rwth-aachen.de/media/
papers/schmitz_2010_egarea_031.pdf
content...
iMindMap - interesting application for displaying hierarchy/relationships between concepts.
Verification of prediction models: which model can we choose in case of emergency?
Jason Haga:
BCP = Business Continuity Plan
ICP = Information Continuity Plan (suggests data-centric plan? just a thought)
Possible outline of report (mostly assembling work we have done) [Do we want
to discuss this?] Here is a draft - probably too much for a report.
o Introductiono Three scenarios: Typhoon, Flood, Tsunamio User-based approach: with the iMindMap
o Three areas of technologies: Monitor, Simulation, Infrastructureo Scenarios from breakout groups (include the slides of short-term goals)
o Actions: Short term and long termo Participants (from material provided to Karen)——————————————————————————————

16