Pitch made by Mr Robert Ogie, Associate Research Fellow, SMART Infrastructure Facility, University of Wollongong on Day 2 of ISNGI 2016.

1. Managing hydrological infrastructure assets
for improved flood
mitigation in coastal mega-cities of developing
nations: From data-starved
to data-driven approach
Robert Ogie
Smart Infrastructure Facility, University of Wollongong
September, 2016

2. Source: Adi Weda EPA

3. Flood losses
• 890 million city residents are currently exposed to natural
disasters, including flooding
• Average annual global flood losses: $6 billion (2005)
$1 trillion (2050)
• The economic losses arising from natural hazards, including
floods, are approximately 20 times higher in developing
countries than developed nations
• Average number of victims is 150 times greater in
developing nations than developed countries

4. Source: Twitter @ TMCPoldaMetro

6. Flood exposure in coastal mega-cities of
developing nations
• Climate change (……frequent, high intensity rainfall)
• Subsiding land (urban dwellings lying below sea-level)
• Population explosion (high settlement density)
• Rapid urbanization (low water permeability, trash dumps in
water bodies)
• Shortage of funding
• Poor infrastructure management (infrequent and
inadequate maintenance of the aging flood control
infrastructure e.g. pumps and floodgates)
• data scarcity (suboptimal flood control decisions)

8. Research question
How can coastal mega-cities in developing nations
improve flood mitigation through data-driven
management of their hydrological infrastructure
assets?
Tap into and explore the unprecedented sources of urban
data that are obtainable in heterogeneous networks.

9. The physical network of
hydrological infrastructure
assets (i.e. waterways,
floodgates, pumps, flood
gauges, etc.)
628 edges representing
rivers, streams, and canals
in Jakarta.
Total geometric length of
waterways is 1092 km.
560 nodes with 96 of those
representing infrastructure
(55 pumps, 30 floodgates,
and 11 flood gauges)
464 network junctions (e.g.,
river confluences)

10. Ogie, R., Holderness, T., Dunbar, M. and
Turpin, E., 2016.
Spatio-topological network analysis of
hydrological infrastructure as a decision
support tool for flood mitigation in
coastal mega-cities.
Environment and Planning B: Planning
and Design, p.0265813516637608.
Application case 1

12. Broken Canal Wall

13. Broken Canal Wall

14. 𝐹𝑉𝐼 =
𝐸∗𝑆
𝑅
(1)
𝐸 = 𝑖=1
𝑛
𝑙𝑖 (2)
𝑆 =
1
𝐶 𝑔
(3)
𝑅 = 𝑅 𝑠 + 𝑖=0
𝑚 𝑐 𝑖
𝑙 𝑖∗𝑏 𝑖
(4)
𝐻𝐼𝐹𝑉𝐼 = 𝑖=1
𝑛
𝑙 𝑖
𝐶 𝑔(𝑅 𝑠+ 𝑖=0
𝑚 𝑐 𝑖
𝑙 𝑖∗𝑏 𝑖
)
(5)
Application case 2:
Ogie, R., Holderness, T., Dunn, S. and Turpin, E., 2016.
Assessing the vulnerability of hydrological infrastructure to flood damage in coastal
cities, International Conference on Smart Infrastructure and Construction (ICSIC),
Cambridge, UK, 27 - 29 June 2016
Notation
FVI = Flood Vulnerability Index
E = Exposure
S = Susceptibility
R = Resilience
HIFVI = Hydrological Infrastructure Flood
Vulnerability Index
A
G1
G3
G2
B
G
D
E
C
F
Flowdirection

15. VH= Very High (0.8 -1.0), M = Medium (0.4 - 0.6), L = Low (0.2 -0.4), and VL = Very Low (0 -0.2).
Name of floodgate Susceptibility Resilience Exposure HIFVI Ranking
Sunter C 1.00 0.45 199.89 1.000 VH
Ciliwung Lama 1.00 0.05 133.65 0.922 VH
Kebon Baru 1.00 0.00 122.59 0.890 VH
Muara Angke 0.50 0.43 203.95 0.519 M
Cakung Drainase 0.33 0.00 164.23 0.398 L
Karet 2 0.50 0.39 150.31 0.392 L
Pasar Ikan 0.25 0.51 307.40 0.371 L
Hailai 0.50 0.78 169.77 0.347 L
Istiqlal 0.33 0.13 160.47 0.345 L
Tangki 0.50 0.94 164.02 0.308 L
Jembatan Merah 0.25 0.10 163.09 0.269 L
Citra Land 0.33 0.75 153.79 0.213 L
Cengkareng Drain 0.25 0.00 101.23 0.184 VL
Pulogadung 0.17 0.00 143.50 0.174 VL
Ancol 0.20 0.52 176.82 0.170 VL
Pekapuran 0.20 0.64 170.12 0.151 VL
8 0.13 0.26 151.23 0.109 VL
Sogo 0.50 0.53 36.24 0.086 VL
Poglar 0.33 0.00 33.81 0.082 VL
Warung Pedok 0.50 0.00 12.81 0.046 VL
Manggarai 0.33 0.19 21.79 0.044 VL
Setia Budi 0.33 0.15 19.70 0.041 VL
Minangkabau 0.50 0.64 15.94 0.035 VL
Kampung Gusti 0.50 6.25 34.29 0.017 VL
Kalimati 0.50 0.00 3.04 0.011 VL
Honda 0.17 0.00 6.84 0.008 VL
Duri 0.33 0.00 3.09 0.007 VL
Karet 0.25 62.26 150.34 0.004 VL
Sunter Utara 0.25 0.00 0.92 0.002 VL
Kali Cideng 0.33 5524.27 150.35 0.000 VL
Results for application case 2

17. Application case 3:
Ogie, R., Dunn, S., Holderness, T. and Turpin, E., 2016.
Assessing the vulnerability of pumping stations to trash blockage in coastal mega-
cities of developing nations, Sustainable Cities and Society (in press)
𝐹𝑉𝐼 =
𝐸∗𝑆
𝑅
(1)
𝐸 = 𝑖=1
𝑛
(𝑙𝑖∗ 𝑤𝑖) (2)
𝑙𝑖
𝑒
= 𝑑𝑖 + 𝑙𝑖 − 𝑑𝑖 ∗ 𝑑 𝑖
𝑙 𝑖
(3)
𝐸 = 𝑖=1
𝑛
(𝑙𝑖
𝑒
∗ 𝑤𝑖) (4)
𝑆 =
1
𝐶 𝑔
(5)
𝑅 = 𝑅 𝑚 + (𝑛 − 1) (6)
𝑇𝐵𝑉𝐼𝑙𝑜𝑐𝑎𝑙 = 𝑖=1
𝑛
(𝑙 𝑖
𝑒
∗𝑤 𝑖)
𝐶 𝑔(𝑅 𝑚+(𝑛−1))
(7)
𝑇𝐵𝑉𝐼 𝐺𝑙𝑜𝑏𝑎𝑙 = 𝑖=1
𝑛
(𝑙 𝑖,𝑡𝑜𝑡𝑎𝑙
𝑒
∗𝑤 𝑖)
𝐶 𝑔(𝑅 𝑚+(𝑛−1))
(8)
Direction of Flow
P2
P1
Section 1 Section 2 Section 3

18. 35
65
11
40
1
57
45
34
38
44
54
37
67
28
69
20
15
33
46
6
61
43
18
58
9
42
41
14
71
30
53
29
10
19
50
31
36
7
3
27
52
25
8
47
63
56
66
13
62
51
17
39
55
26
4
68
48
16
64
70
60
2
24
12
22
5
21
59
32
49
23
LocalTBVI GlobalTBVI
Results for application case 3

19. Sensor networks
Given a limited number of
water level sensors, where
should they be placed in
such a complex hydrological
infrastructure network?
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Sensor n
𝐹1 𝑆 =
𝑣∈𝑆
𝑓1(𝑣)
𝑓1(𝑣) = 0.7(𝑛 𝑣
𝑢 + 𝑛 𝑣
𝑑) + 0.2c 𝑣 + 0.1 𝑖=0
𝑛
𝑑𝑒𝑔− (𝑣)
𝑓2 𝑣 =
𝑢∈𝑆
𝑑(𝑣,𝑢)
𝐹2 𝑆 =
𝑣∈𝑆 𝑢∈𝑆
𝑑(𝑣,𝑢) =
𝑣∈𝑆
𝑓2 𝑣
𝑑(𝑣,𝑡)
𝑒
> 𝜆 ∀ 𝑣 ∈ 𝑆, 𝑡 ∈ 𝑇
Using 𝐹1 𝑆 and 𝐹2 𝑆 with a
constraint, optimal positions are
computed for placement of a given
number of sensors in the
hydrological infrastructure network.

20. Jakarta- The no. 1
biggest user of twitter
Over 100,000 #flood
tweets
Social networks

22. www.petajakarta.org

25. Recent observations put robustness and sustainability as key
issues with the use of social media data for urban decision
making
In 60 days of the 2014/2015 monsoon season
(1,000 confirmed reports )
(350 confirmed reports )
In 270 days of the 2015/2016 monsoon season

26. Concluding remarks
How can coastal mega-cities in developing nations improve flood mitigation through
data-driven management of their hydrological infrastructure assets?
1. How can physical network data be used in data-driven management of
hydrological infrastructure assets in order to improve flood mitigation in
coastal mega-cities of developing nations?
2. How can sensor network data be used in data-driven management of
hydrological infrastructure assets in order to improve flood mitigation in
coastal mega-cities of developing nations??
3. How can social network data be used in data-driven management of
hydrological infrastructure assets in order to improve flood mitigation in
coastal mega-cities of developing nations??????
4. How can the cyber (sensor), physical and social network layers be integrated
to attain a self-organising social-technical system that reliably and
sustainably provides urban data to municipal authorities responsible for the
management of flood control infrastructure in coastal mega-cities????????