Managing hydrological infrastructure assets for improved flood

mitigation in coastal mega-cities of developing nations: From data-starved

to data-driven approach

Robert OgieSmart Infrastructure Facility, University of Wollongong

September, 2016

Source: Adi Weda EPA

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

Source: Twitter @ TMCPoldaMetro

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)

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.

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)

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

Broken Canal Wall

Broken Canal Wall

(1)

(2)

(3)

(4)

(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

NotationFVI = Flood Vulnerability IndexE = ExposureS = SusceptibilityR = Resilience HIFVI = Hydrological Infrastructure Flood Vulnerability Index

A

G1

G3

G2

B

G

D

E

C

F

Flow

dire

ctio

n

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

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)

(1)

(2)

  (3)

(4)

 

(5)

(6) 

(7)

  (8)

 

Direction of Flow

P2P1

Section 1 Section 2 Section 3

356511401

57453438445437672869201533466

614318589

42411471305329101950313673

2752258

47635666136251173955264

6848166470602

2412225

2159324923

Local TBVI Global TBVI

Results for application case 3

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

𝐹 1 (𝑆 )=∑𝑣∈ 𝑆

𝑓 1(𝑣)

𝑓 2 (𝑣 )=∑𝑢∈𝑆

𝑑(𝑣 ,𝑢)

𝐹 2 (𝑆 )=∑𝑣∈𝑆

∑𝑢∈𝑆

𝑑(𝑣 ,𝑢)=∑𝑣∈ 𝑆

𝑓 2 (𝑣 )

𝑑(𝑣 ,𝑡 )𝑒 >𝜆∀𝑣∈𝑆 ,𝑡∈𝑇

Using and with a constraint, optimal positions are computed for placement of a given number of sensors in the hydrological infrastructure network.

Jakarta- The no. 1 biggest user of twitter

Over 100,000 #flood tweets

Social networks

www.petajakarta.org

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

Concluding remarksHow 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????????