The Roles of Network Analyses in Optimizing the Number and Locations of Fire Brigade Posts in Mitigating Peatfires
Corresponding email: ari.sandhyavitri@lecturer.unri.ac.id
Published at : 16 Dec 2019
Volume : IJtech
Vol 10, No 8 (2019)
DOI : https://doi.org/10.14716/ijtech.v10i8.3398
Sandhyavitri, A., Sutikno, S., Sahputra, R., Amri, R., Widodo, H., Husaini, R.R., Seto, T.H., 2019. The Roles of Network Analyses in Optimizing the Number and Locations of Fire Brigade Posts in Mitigating Peatfires. International Journal of Technology. Volume 10(8), pp. 1548-1557
| Ari Sandhyavitri | Department of Civil Engineering, Faculty of Engineering, Universitas Riau, Pekanbaru City, Riau 28292, Indonesia |
| Sigit Sutikno | Department of Civil Engineering, Faculty of Engineering, Universitas Riau, Pekanbaru City, Riau 28292, Indonesia |
| Rizki Sahputra | Department of Civil Engineering, Faculty of Engineering, Universitas Riau, Pekanbaru City, Riau 28292, Indonesia |
| Rayhul Amri | Department of Civil Engineering, Faculty of Engineering, Universitas Riau, Pekanbaru City, Riau 28292, Indonesia |
| Heru Widodo | BPPT, Building of Ir. Soebagio – Geotech PUSPIPTEK, Kawasan Puspiptek Gedung 220, Tangerang, Banten 15314, Indonesia |
| Tri Handoko Seto | BPPT, Building of Ir. Soebagio – Geotech PUSPIPTEK, Kawasan Puspiptek Gedung 220, Tangerang, Banten 15314, Indonesia |
| Rizki Ramadhan Husaini | Department of Civil Engineering, Faculty of Engineering, Abdurrab University, Jalan Riau Ujung 73, Pekanbaru City, Riau 28292, Indonesia |
Quick responses in managing peatfire disasters in
Indonesia are one key success in mitigating and controlling the risk of
peatfire disasters. The aim of this study was to simulate and identify the
optimum numbers and locations of fire brigade posts for improving their
response times in mitigating peatfire events in Bengkalis Island, Riau,
Indonesia. Network analyses were applied in the case of peatfire events on this
island. The results of this study may assist local governments and fire brigade
teams in developing a strategy to manage peatfires systematically. Hence, the
results may contribute to the body of knowledge as a reference in
systematically controlling peatfire disasters elsewhere in the world. This
study proposed five steps in the identification of appropriate locations of
fire brigade posts and performs two main steps to achieve this objective: (i)
evaluating existing fire brigade posts’ service coverages; and (ii) developing
three scenarios for simulating additions of one, two, and three posts. The
results of this study improve fire brigade dispatch time performances as well
as expand their service coverage areas from 40.7% to 62.4% within 60 minutes of
dispatch time.
Bengkalis; Brigade posts; Dispatched time; Network analyses; Optimizing; Peatfires
Figure 1 Research location on Bengkalis Island, Indonesia
A
number of peatland fire incidents on Bengkalis Island have occurred in the
period from 2012 to 2016 (WWF, 2016). The fire incidents spread to two main
locations: the middle western and southern areas (Figure 2). Two existing fire
brigade posts were located in the District of Bengkalis and Bantan in 2019
(Figure 2b). The objective of this article is to investigate to what extent
network analyses may assist in simulating and optimizing the number and
locations of fire brigade posts in mitigating peatfires. In the period from
2013–2016, the greatest number of fires (983) occurred in 2014 (WWF, 2016)
(Figure 2a). Various efforts have been conducted to mitigate these fire
disasters, such as establishing two fire brigade posts. However, it has been
acknowledged that the number of these fire brigade posts was too limited to
cover the entire island area (WWF, 2016) (Figure 2b).
In fact, no
research has been conducted to optimize the appropriate number and locations of
fire brigade posts to mitigate peatfire impacts in Indonesia. Hence, there was
a need to conduct research to identify the appropriate number of brigade posts
and their locations using network analyses.
Figure 2 (a)
Hotspot locations from 2013–2016 in Bengkalis, Indonesia; and (b) existing fire
brigade posts and road networks in 2017
The island’s
government and the Local Disaster-prevention Agency (Badan Penanggulangan
Bencana Daerah; BPBD) must develop a strategic plan for selecting
appropriate locations for fire brigade posts in the case of fire disaster
events in a large coverage area in time deployment of under 1 hour. This study could be used as a reference in
systematically mitigating peatfire disasters, not only in Indonesia but across
the world.
The application of various methods for evaluating and
identifying appropriate routes, locations, and destinations have been reviewed
in several publications, including spatial and travel time relationships, a
digital route guided map approach, possible shortest traveling times, and
network analyses (Kumar et al., 2014; Sutikno & Murakami, 2015; Beere,
2016; Ranya et al., 2016). This study thus applies network analyses as a method
to evaluate and calculate fire brigade dispatch times in Bengkalis, as this
application is well-known, generally adopted, and proven to help solve cases of
route analysis.
Beere (2016) reviewed applications of road network
layer analysis that encompassed spatial and travel time relationships to
identify the shortest road access to the nearest health service locations under
the uncertainty of traffic congestion. Ranya et al. (2016) explored digital
route–guided maps capable of locating health services within a designated area
in emergency cases by the application of geographic information systems (GIS)
using network analysis based on the shortest possible traveling time under time
constraints in Khartoum, Sudan. Kumar et al. (2014) evaluated the safest,
fastest ways to transport students from their homes to schools in India.
Various variables were considered in the network analyses, including street
networks, travel time, speed, and turning movements. The application of GIS
software assisted this study to find optimal routes to reach designated
schools. Sutikno and Murakami (2015) also used network analyses to evaluate and
optimize shelter locations in the case of tsunami evacuation in Japan. A number
of service areas for safe tsunami evacuation caused by earthquakes in Indonesia
were also discussed in this study, which considered the combination of spatial
and network analysis using GIS to improve tsunami mitigation measures.
Thawongklang and Tanwanichkul (2016) evaluated an application of
production-scheduling techniques for dispatching ready-mixed concrete and
indicated that delivery delays could be minimized by improving delivery times,
cutting operational costs. This study also used network analyses that
encompassed various data inputs, such as distances, travel speed, and travel
times, determined by GIS software (ArcGIS). The results have been proven to
improve the delivery process performance.
Few studies have reviewed the application of network analyses in improving
fire brigade performance, however, especially in abating peatfire disasters. This study utilizes network analyses to optimize the
number and locations of fire brigade posts in mitigating peatfire disasters
based on GIS. As mentioned, these analyses are common and widely applied in the
evaluation and identification of appropriate routes and locations of designated
areas. Network
analysis, one of the methods provided by GIS software, finds least-impeded
paths, such as finding the shortest road networks to transport students to
their schools or fire brigade teams to fires (Fischer, 2014; Longley et al.,
2001; Miller & Shaw, 2001 Sutikno & Murakami,
2015). Network analysis creates a
network dataset and analyzes the network, a method that builds on several
ArcGIS virtues, such as ArcCatalog for creating a network dataset, ArcMap for
analysis, and ArcMethodbox for geo-processing (Fischer, 2014;; Kumar et al.,
2014). The relationship between nodes and arcs in the network analysis is known
as network topologies (Fisher, 1995).
Network
analysis may assist governments in developing strategies for improving service
coverage areas of fire-prevention teams for constrained dispatch times. Based
on the network area analyses obtained from the ArcGIS, ArcCatalog, ArcMap, and
the ArcMethodbox, the geo-processing results show that the two existing fire
brigade posts have service coverage in Bengkalis Island of only 40.7%. This
study simulated the additions of one, two, and three fire brigade posts, which
improved fire brigade service coverage by 55% (from 40.7% to 63.4%).
The author would like to thank the Bengkalis Regency and the Civil Engineering Department of the University of Riau for providing facilities and map data for this article. We also express our gratitude to Insentif Riset Sistem Inovasi Nasional (INSINAS) research grants for providing financial support for this research.
Beere, P., 2016. Creating a Road Network Analysis
Layer with Travel Time Estimates using Open-source Data. GeoHealth Laboratory,
New Zealand, Report, pp. 1–18, Available Online at
http://www.geohealth.canterbury.ac.nz/working/beere2016.pdf
Desrosiers, J., Dumas, Y., Solomon, M.M., Soumis,
F., 1995. Time-constrained Routing and Scheduling. In: Handbooks in
Operations Research and Management Science, Ball, M.O., Magnanti, T.L., Monma,
C.L., Nemhauser, G.L. (eds.), Elsevier, Amsterdam, Volume 8, pp. 35–139
Fadlalla Abdalla Elsheikh, Ranya., Elhag,
Abdelrhim., Eddeen Khidir Sideeg, Salah., Elhadi Mohammed, Aisha., Ali Gism,
Nagla., Sharif Abd Allah, Mohammed., 2016. Route Network Analysis in Khartoum
City. SUST Journal of Engineering and Computer Science (JECS), Vol. 17, No. 1.
Fischer, M.M., 2014. GIS and Network Analysis. In: Handbook 5 Transport Geography and
Spatial Systems. Hensher D., Button K., Haynes K. and Stopher P. (ed.),
Pergamon
Fisher, M., 1995. Vehicle Routing. In: Handbooks in Operations Research and
Management Science, Volume 8, Ball, M.O., Magnanti, T.L., Monma, C.L.,
Nemhauser, G.L. (ed.), Elsevier, Amsterdam, pp. 1–33
Goodchild, M.F., 2001. GIS and Transportation:
Status and Challenges. GeoInformatica,
Volume 4(2), pp. 127–139
Harrison, M.E., Page, S.E., Limin, S.H., 2009. The
Global Impact of Indonesian Forest Fires. Journal
of Biological Education, Volume 56(3), pp. 156–163
Haryono, 2011. Indonesia Peat Land Map (Peta Lahan Gambut Indonesia), Scale 1:250,000. Jakarta
Kumar, N., Kumar, M., Denis, D.M., Srivastava,
S.K., Srivastva, O.S, 2014. Geospatial School Bus Routing. International Journal of Engineering and Science (IJES),
Volume 3(11), pp. 80–84
Longley, P.A., Goodchild, M.F., Maguire, D.J.,
Rhind, D.W., 2001. Geographic Information
Systems and Science. Chichester: John Wiley
Miles, L., Ravilious, C., García-Rangel, S., Lamo,
X.D., Dargie, G., Lewis, S., 2017. Carbon,
Biodiversity and Land-use in the Central Congo Basin Peatlands. Cambridge,
UK: United Nations Environment Programme
Miller, H.J., Shaw, S-L., 2001. Geographic Information Systems for Transportation:
Principles and Applications. Oxford: Oxford University Press
Public Works (PU), 2009. Regulation No. 20/2009, Technical Guidelines
for Urban Fire Protection Management. Jakarta
Sandhyavitri, A., Perdana, M.A., Sutikno, S.,
Widodo, F.H., 2017. The Roles of Weather Modification Technology in Mitigation
of the Peatfires During a Period of Dry Season in Bengkalis, Indonesia. In: IOP Conf. Series: Materials Science
and Engineering, Volume 309
Sutikno, S., Murakami, K., 2015. Application of
Spatial and Network Analysis to Evaluate Shelter Plan for Tsunami Evacuation. Civil Engineering Dimension, Volume
17(2), pp. 88–94
Thawongklang, K., Tanwanichkul, L., 2016.
Application of Production Scheduling Techniques for Dispatching Ready-mixed
Concrete. International Journal of
Technology, Volume 7(7), pp. 1163–1170
Wahyunto, S.R., Subagjo, H., 2003. Peat Land and
Carbon Content Map for Sumatra (Peta Luas
Sebaran Lahan Gambut dan Kandungan Karbon di Pulau Sumatera) 1990–2002,
Book 1. 1st Edition. Wetlands International – Indonesia Programme,
pp. 13–15
World Wide Fund for
Nature (WWF), 2016. Forest Fire. WWF Indonesia