This document discusses how geosocial intelligence and spatio-topological network analysis of hydrological infrastructure can assist with flood mitigation efforts in coastal mega-cities. It provides examples of analyzing the vulnerability of floodgates and pumping stations to failure using models that consider exposure, susceptibility, and resilience. The analysis can help prioritize maintenance of vulnerable infrastructure. Moving this network analysis to a global flood condition model and addressing key research questions could further improve proactive flood response.

1. Geosocial intelligence: assisting flood
mitigation
Robert Ogie
Smart Infrastructure Facility, University of Wollongong
May, 2016

2. Source: Adi Weda EPA

4. Source: Twitter @ TMCPoldaMetro

11. Flood exposure in coastal mega-cities
• 890 million city residents are currently exposed to natural disasters, including
flooding
• Average annual global flood losses: $6 billion (2005) $1 trillion (2050)
• Climate change + subsiding land + population explosion + rapid
urbanization = increased exposure to flood hazards
The situation is far more complicated in developing nations
• Climate change + subsiding land + population explosion + rapid
urbanization + infrastructure fragility + human factors (e.g. trash dumps) +
data scarcity = increased exposure to flood hazards

14. Broken Canal Wall

15. Broken Canal Wall

19. Pilot Study 2014/2015
60 days:
100,000 #flood tweets
1,000 confirmed reports
69,000 users
2.2m Twitter impressions

20. www.petajakarta.org

24. Proactive response to flood disasters
• How can we make more informed decision related to the use of pumps
and floodgates to control floods, such that excessive pressure created by
accumulating floodwater does not result in structural failure and flooding
due to infrastructure fragility?
• How can we identify the floodgates that are most vulnerable to failure or
damage due the impact of flood waters, so that limited resources can be
judiciously allocated for maintenance and flood preparedness?
• How can we identify the pumping stations that are most vulnerable to
failure due to trash blockage, so that limited resources can be judiciously
allocated for maintenance and flood preparedness?

25. 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)

26. 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

27. Application case 1 continues……

28. 𝐹𝐹𝐼 =
𝐸∗𝑆
𝑅
(1)
𝐸 = ∑ 𝑙𝑖
𝑛
𝑖=1 (2)
𝑆 =
1
𝐶 𝑔
(3)
𝑅 = 𝑅 𝑠 + ∑
𝑐 𝑖
𝑙 𝑖∗𝑏 𝑖
𝑚
𝑖=0 (4)
𝐻𝐻𝐻𝐻𝐻 =
∑ 𝑙 𝑖
𝑛
𝑖=1
𝐶 𝑔(𝑅 𝑠+∑
𝑐 𝑖
𝑙 𝑖∗𝑏 𝑖
𝑚
𝑖=0 )
(5)
Application case 2: Vulnerability analysis of hydrological infrastructure to flood
hazards
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

29. 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

31. Application case 3: Vulnerability of pumping stations to trash blockage
𝐹𝑉𝑉 =
𝐸∗𝑆
𝑅
(1)
𝐸 = ∑ (𝑙𝑖∗ 𝑤𝑖)𝑛
𝑖=1 (2)
𝑙𝑖
𝑒
= 𝑑𝑖 + 𝑙𝑖 − 𝑑𝑖 ∗ 𝑑 𝑖
𝑙 𝑖
� (3)
𝐸 = ∑ (𝑙𝑖
𝑒
∗ 𝑤𝑖)𝑛
𝑖=1 (4)
𝑆 =
1
𝐶 𝑔
(5)
𝑅 = 𝑅 𝑚 + (𝑛 − 1) (6)
𝑇𝑇𝑇𝑇𝑙𝑙𝑙𝑙𝑙 =
∑ (𝑙 𝑖
𝑒
∗𝑤 𝑖)𝑛
𝑖=1
𝐶 𝑔(𝑅 𝑚+(𝑛−1))
(7)
𝑇𝑇𝑇𝑇 𝐺𝐺𝐺𝐺𝐺𝐺 =
∑ (𝑙 𝑖,𝑡𝑡𝑡𝑡𝑡
𝑒
∗𝑤 𝑖)𝑛
𝑖=1
𝐶 𝑔(𝑅 𝑚+(𝑛−1))
(8)
Direction of Flow
P2
P1
Section 1 Section 2 Section 3

32. 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

33. 0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 50000 100000 150000 200000 250000
GlobalTBVI
Exposure, E

35. Way forward
• Move network analysis from local to global flood condition
• Develop flood inundation model (DATA?)
Key research questions?
• How does one foster “odd time” sensing?
• How can manipulative effect and truthfulness be checked?
• How many human sensors can cover the city? How can one
ensure area coverage requirement is always met?
• Standardisation of crowdsensed flood height
• How can citizens be motivated to participate proactively in
sensing flood?
• Does absence of tweets always indicate no floods; what is the
check?
• Should participants be given incentives?
• What type of incentives?

36. Conclusion
Q& A