By using IoT which is water level sensor node to predict water level of the river and Flood control using Rain Garden monitored IoT sensors.

1. IoT-DRR in Hii River Basin for Flood
Prediction and Control with Green
Infrastructure
Toshiyuki Moriyama (Fukuoka Inst. of Tech., Japan)
Katsuhiro Morishita
(Kumamoto College, National Inst. of Tech., Japan)
Shinobu Izumi (sitateru Co, Ltd., Japan)
Koji Nishiyama (Kyushu Univ., Japan)
Jun Teramura (Kyushu Univ., Japan)
Fumiko Taura (Kyushu Univ., Japan)

2. IoT?

3. IoT?
Internet
Mainly connected now Connect in the Future
Internet of Things

4. What is IoT-DRR?
Internet of Things
Sensors and Actuators→Collecting Data
and Act Something
IoT
DRR
Disaster Risk Reduction
Cheap water level sensor for flood→
Predict the flood
Vibration sensor → Detect debris flow

5. GI:Green Infrastructure
Example of Grey Infrastructure
Shimatani and Taura, Kyushu Univ.
Example of Green Infrastructure

6. Background

7. Fukuoka City
Study Area
Asakura,Toho,
Hita
Kyushu Island

8. Flooding in Fukuoka-city
• 29th May 1999
Heavy Rainfall at Fukuoka-
prefecture
• 19-21 July 2003
Heavy Rainfall at Kyushu District
• 19-26 July July 2009
Heavy Rainfall at Chugoku and
Kyushu District
Flooding @Hii River 24th July, 2009
One person dead in underground
And two serious draught were available
in Fukuoka city.

9. Urbanaizing at Fukuoka-city
Urbanized Ratio of Hii River Basin:70% (24km2)
1900 Now

10. http://www.mlit.go.jp/kisha/boshu/boshu42/kouka02_02.pdf
From Website of MILT, Japan
Before the Development
Most of Rainfall reserves in
paddy field and flow into ground
water i.e. outlet of rainwater to
river is saved.
After the Development
Due to the covering ground
surface by concrete or asphalt
and disappearance of forest and
paddy field, amount of outlet to
river increase and flood will be
occur frequently.

11. Obsereved flooding area
(2009 july 24th)
Made by R. Watanabe et al.(2009)
Dike break
From sewer system

12. July 24, 2009 Hii River （near Tajima Bridge）
96mm/1hour, Total:196mm, Maximum Water Level 4.24m
Water
Level
Rainfall
TimeMade by R. Watanabe et al.

13. Objectives
For water resources → Views how amount
rainwater reserved (ex. after the earthquake)
Raingauge network
Control the flood in urban area
Using a Smart Rainfall Tanks with Sensor+River
water level sensor =>DRR
may possible… We propose “Rainwater Social”

14. What is Rainwater Social？
• In rice cultivating piscatory civilizations、
from ancient ages, management of water
had been made by residents.
• After that, due to the modernization of
the Meiji Restoration, a centralized
government of Western style was
imitatively formed, management of water
supply, sewerage and river was entrusted
to the government and local governments.
• Residents used water supply and sewage
without consciousness. Residents lost the
consciousness for circulation of the water.
Only when it gets flooded, the residents
blame "Why flooding was happen?" to the
government.
To resume the consciousness for circulation of the water
This is a god of water that is enshrined in the lower stream of Akatani
River in Asakura-city where attacked by heavy rainfall 2017.
God of Water

15. For Example, in Asakura
Residents will exploit the construction
of weirs and waterways to counter
drought, as depicted in the historical
novel "God of Water .
Instead of doing anything, the clan
prepared crucifixion for readers of
residents.
In the water conflict, four masonry
weirs were made in the Edo period
(1602-1868). Three weirs are present.
amazon.co.jp
God of Water

16. The weir of the mason is living today
Local people can not repair in concrete,
that is, they are not sustainable.
http://yamadazeki.net/template_a-5-9
Asakura Afganistan

17. Store and Infiltration
Rain GardenRain Garden
Rainwater Storage
Evaporation
Rainwater
Harvesting
Permeable box
Waste Water Pipe System
Distributed Multi-purpose Civil Dam
Infiltration
Infiltration
Waste Water Main Pipe
To Sewage treatment plant
Overflow to river when flood
Shimatani et. al
The rainwater is stored and infiltrated, and
distributed water management that does not
enter the underground pipe or river at once.
An organic society with water and green
supported by residents.
Rainwater Social

18. Rainwater Social
Grey Infrastructure
Green Infrastructure
Shimatani et. al
Before measures
After measures
Rainwater pipe sewege
Rainwater pipe sewege
Overflow from manhole
Reduction of Overflow from manhole
Rainwater Tank
Measures to control outflow in the community
Soil improvement
Infiltration

19. Labs. for Rainwater social
Save Rainwater!
Shimatani at el.
Rainwater Saving Heroes
Enlightenment at various events
6 University Gathered for This Project

20. Rainwater Saver’s Car
Labs. for Rainwater social

21. Mizube Ring Hii River
Cheers to the Waterside @7th July 7:07PM
Promote to residents
for join us

22. details of 41 % saving
ponds for agriculture
schools
parks
public
facilities
vacant lot
housing and others
discharge to river and
stormwater sewer
Proposal to local government

23. 5tons or more tanks are needed
for control of urban flash flood
under the car port
under the ground of house
under the balcony at first floor
spacer

24. Rainwater tank installed at
Moriyama’s house
under the car port

25. 森山邸に雨水タンク設置

26. LoRaWAN Gateway
Low Power
Long Range
Wide Area Net
Convivial Center for Rainwater Harvesting starts 2017

27. ２km
LoRaWANカバー域
あめにわ憩いセンター
渡辺邸
西新保育園
1.5km
Cover Area with LoRaWAN (20mW)
Already installed

28. Water Level Sensor for Rainwater Tanks
Calibration
30cm eTape

29. Rainwater tanks group 1 for Convivial Center for
Rainwater Harvesting(Fig. by Fumiko Taura)

30. Observation of infiltrate capability in Convivial Center for Rainwater Harvesting
(Fig. by Fumiko Taura)

31. Rainwater tanks group II for Convivial Center for Rainwater
Harvesting(Fig. by Fumiko Taura)

32. Rainfall Event Captured by Online Water Level Sensor

33. Rainfall Event Captured by Online Water Level Sensor
This rain garden is infiltrated for19.6mm/hour rainfall,
but we expect over 100mm/hour →Hope to capture this rainy season…

34. S1
S2
S3
Display with Kibana from the sensors

35. Northern Kyushu Heavy Rainfall
@5th July 2017
Heavy Rainfall (1/150yr. or more,
129.5mm in 1Hr)
→Plenty of Landslide(over 1,000 points)
→Huge amount of Trees, Earth and Sand
→Overflow at Bridge→10x wider river
→Houses destroyed by Trees and Sands
→Over 40 peoples were killed
Asakura, Toho, Hita was attacked →about 50km far from Fukuoka-city

36. Maximum 3hours Accumulated
Rainfall Amount and Landslide
Rainfall is based on X and C band MP radar (XRAIN)
operated by MILT, Japan
About 400km2 area

37. Mid-stream of Akatani River
in Asakura-city

38. Downstream of Akatani River in
Asakura-city

39. Making 360VR for Disaster Archive
Investigation @Shirakidani River
River bed were 10x or more wider

40. You can see throughVirtual Goggles
Making 360VR for Disaster Archive

41. 災害アーカイブ
朝倉市白木谷川災害調査(2017 07/31)

42. Rainall:Northern Kyushu Heavy Rainfall@july 2017
A small residential area in Tokyo
Before measures After measures
Shimatani et. al
Rain garden, Permeable pavement sidewalk, Bioswale,Parking area,
School, Park → full performance
An Example of Simulation for Green Infrastructure

43. 0
50000
100000
150000
200000
• 98
0
50
100
150
200
250
3000
50000
100000
150000
200000
250000
300000
350000
400000
0:10 3:10 6:10 9:10 12:10 15:10 18:10 21:10 0:10
(mm/hr)
()
11.6mm/5min
12.5 /5min
An Example of Simulation for Green Infrastructure
Before measures After measures
Shimatani et. al
Inundation(m3)
Inundation area(m2)
Before measures
After measures
Rainfall
The amount of flooded water can be reduced by 98%
Rainfall amount(mm)
Even if it rains 11.6 mm in 5 minutes it will not submerge
unless it continues
If it rains 12.5 mm continuously in 5 minutes it will
flood, but on a small area
The flooded area is settled constant because of the depression pool

44. Water Level
Observation and
Prediction
for Urban Rivers
using IoT sensors

45. http://map.flood.network/
In North England,
Already used LoRaWAN
Water level monitoring using LoRaWAN on grass-roots human
network is underway->But what about forecasting?
http://flood.network/
For Flood water
level observation
Using Ultrasonic sensor

46. Principals of Prediction for Water Level
In Flash Flood (Hirano et al. 1986)
Concentration Time τ21
Catchment area of
Shirakawa river
ΔA2 (t + I) = kΔA1(t)+ R(t)
No need to use Q
2 1

47. An example of prediction for Shirakawa River
with Kalman filter small foot print→

48. Calculated at Sendaigawa River（Hirano et. Al 1988）
Prediction of precipitation(=real data) used、lead time=3hours
Water level station is 2.
No precipitation used、lead time=3hours
Water level station is 2.
Sendaigawa
catchment
Concentration Time τ21
Optimize with Kalman filter
Water Level Water Level

49. Used 5 water level stations
No precipitation used
To set the lead-time <= concentration time of slope channel,
it is no need to use uncertainly rainfall predictions
Calculated at Sendaigawa River（Hirano et. Al 1988）
lead time=3hours
Prediction of precipitation(=real data) used

50. Display by Elastic Search and Kibana
Using Ultrasonic
Sensor
Water Level Sensors In Hii RiverWater Level Sensors In Hii River

51. Conclusion
Water level of Rainwater Tanks and River is
observed and Send a data to Cloud with
LoRaWAN.
Rain Garden infiltrated 19.6mm in 1 hour
rainfall amount, but expect over 100mm in
1hour.

52. Future works
Make 8 water level stations on Hii River Basin (now 3 stations)
Make a More Long Range LoRa system for DRR (1.5km cover range
is too small)
Make a lightweight online&realtime prediction system for
Smartphones and Raspberry Pi.
Support LoRa IoT system by Civic Tech for Asakura, Toho, Hita
Area

53. Thanks for your attention !
This research is mostly supported by the Research Institute of Science and
Technology
for Society (RISTEX), Japan Science and Technology Agency (JST)
under the project "Distributed Rainwater Management for a Sustainable Well-
being Society"
in the R&D focus area "Designing a Sustainable Society through Intergenerational
Co-creation"
Acknowledgment

54. AWS cloud
Amazon
DynamoDB
IoT
sensor
IoT
sensor
JMA
Amazon
S3
Amazon
EBS
Amazon
EC2
AWS
Lambda
AWS
Elastic
Beanstalk
Java
Amazon
S3
HTTP
FTP
Java
Amazon
EC2
HTTP/MQTT
SORACOM Air
（Minimum）
SORACOM
Beam
MQTT
IoT
actuator
Direct
XBee
XBee
NWP result

55. Rainwater house
Prof. Ryoichi Watanabe’s
house
under the house:17ton
under the parking area:25ton

57. Cost
A 60,000 tons rainwater storage facilities around
Hakata station made by Fukuoka city is estimated
as 40 billion yen =>666,667yen/ton
A 42 tons rain water tanks for Prof. Ryoichi
Watanabe’s House is about 4 million yen
=>95,238yen/ton
Latter reduces the cost to 1/7!