Optimizing Search and Rescue Personnel Allocation in Disaster Emergency Response using Fuzzy Logic
Corresponding email: maberawi@eng.ui.ac.id
Published at : 29 Nov 2019
Volume : IJtech
Vol 10, No 7 (2019)
DOI : https://doi.org/10.14716/ijtech.v10i7.3709
Berawi, M.A., Leviakangas, P., Muhammad, F., Sari, M., Gunawan., Yatmo, Y.A., Suryanegara, M., 2019. Optimizing Search and Rescue Personnel Allocation in Disaster Emergency Response using Fuzzy Logic . International Journal of Technology. Volume 10(7), pp. 1416-1426
| Mohammed Ali Berawi | -Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia -Center for Sustainable Infrastructure Development, Faculty of Engineering, Un |
| Pekka Leviakangas | Industrial Engineering and Management, Faculty of Technology, University of Oulu, 90570, Finland |
| Fadhi Muhammad | -Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia - Center for Sustainable Infrastructure Development, Faculty of Engineering, U |
| Mustika Sari | -Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia -Center for Sustainable Infrastructure Development, Faculty of Engineering, Un |
| Gunawan | -Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia -Center for Sustainable Infrastructure Development, Faculty of Engineering, Un |
| Yandi Andri Yatmo | Center for Sustainable Infrastructure Development, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
| Muhammad Suryanegara | Center for Sustainable Infrastructure Development, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
Several models
have been developed to facilitate decision-making in disaster management,
especially in relation to emergency resource allocations. These models are
required in order for search and rescue personnel to operate efficiently.
However, in Indonesia, in general, technology has not been used to help make
decisions during the response phase; rather, these decisions are still made
subjectively. This paper presents a decision-making model that helps search and
rescue teams determine the number of personnel to deploy. Therefore, it
streamlines the allocation of personnel in a search area, and it determines the
number of personnel that are needed based on the area, population density, equipment, and the number of high
buildings. Then, those variables are processed using a fuzzy expert system and
a decision tree. The data and knowledge acquired as a reference were obtained
from disaster management experts as well as experienced practitioners in the
field of Search and Rescue.
Decision tree; Disaster management; Fuzzy expert system; Search and rescue
Indonesia
is situated at the intersection of three major tectonic plates—the
Indo-Australian, Eurasian, and Pacific plates—that collide. Thus, this region
experiences many earthquakes, and tsunamis that occur due to tectonic
activities. An earthquake is a natural disaster, the occurrence of which is
unpredictable even though some variables can indicate if one will happen.
However, until now, these indications have not yet achieved a high accuracy
rate, so regions with a high disaster risk need to devise other methods for
managing an earthquake, considering that it is a disaster that can exact a
severe toll in terms of its impact on the economy, human lives, and physical
damage. Based on risk assessment data reported by the National Disaster
Management Agency BNPB (BNPB, 2016), the social losses that
Indonesia has to bear because of earthquakes include: 86,247,258 lives, IDR
406,689,834 in physical structure losses, and IDR 182,185,171 in economic
losses. Hence, Indonesia needs to have an effective natural disaster management
policy to reduce these risks.
Disaster
management is a series of activities implemented prior to, during, and
following the occurrence of a disaster. There are five disaster phases in the
disaster management cycle:
In Indonesia, the BNPB, a
non-ministerial government agency, is responsible for conducting disaster
management, including the response phase that covers the pre-disaster phase,
the disaster phase, and the post-disaster phase. To fulfil its duty, BNPB is
supported by a search and rescue (SAR) team and volunteers. Based on BNPB’s
2017 performance report, BNPB needs 24 hours post-incident to respond to a
disaster. Moreover, the time needed to disseminate information to the public
regarding the disaster occurrence is about 3 hours. Based on the 2016
performance report released by SAR, the survivor percentage was 79.86%; thus,
there is room for improvement. The response time and survivor percentage are
affected by the capability of the rescue team and the availability of
information regarding the victims.
Several decision-making models are used in disaster
management, especially in regard to emergency resource allocations. They are
required in order for SAR personnel to operate efficiently. However, in
Indonesia, technology has not been used to help make decisions in the response
phase. Furthermore, fuzzy logic and mathematical models have not been used in
disaster management. The 2015 National SAR Agency (BASARNAS) performance report
regarding the percentage of the number of survivors and victims found during
SAR operations, were 83.21% and 96.61%, respectively (BNPB, 2016). The result indicates that
the response efforts made to save victims can be improved. Moreover, because
the number of rescuers is limited, it is essential that personnel allocation be
optimized. While previous studies have investigated similar issues, none have
made use of fuzzy logic. In fact, it is one of the most suitable methods used
for decision making in relation to disaster problems (Öztaysi
et al., 2013).
Fuzzy logic and decision trees can be used to
determine the number of victims trapped during an earthquake. Furthermore, a
model based on these methods can be used to accelerate the planning phase for
SAR operations. However, the accuracy of this model can be improved by defining
the variables in more detail and by adding the existing variables. In the
future, this model can easily be enhanced based on more specific areas or
conditions.
This research was supported by
a European Union (EU) research grant for Building European Communities’
Resilience and Social Capital (BuildERS) project and by a Universitas
Indonesia’s PITTA research grant.
Abi-Zeid, I., Frost J.R., 2005. SARPlan: A Decision Support System for
Canadian Search and Rescue Operations. European Journal of Operational
Research, Volume
162(3), pp. 630–653
Aghamohammadi, H., Mesgari, M.S.,
Mansourian, A., Molaei, D., 2013.
Seismic Human Loss Estimation for an Earthquake Disaster using Neural Network. International
Journal of Environmental Science and Technology, Volume 10(5),
pp. 931–939
Anson, S., Watson, H., Wadhwa,
K., Metz, K., 2017. Analysing Social Media Data for Disaster Preparedness:
Understanding the Opportunities and Barriers Faced by Humanitarian Actors. International
Journal of Disaster Risk Reduction, Volume 21, pp. 131–139
Awange, J.L., 2012. Disaster
Management. In: Environmental Science and Engineering
(Subseries: Environmental Science). pp. 271–306
Berawi, M.A., 2018. The Role
of Technology in Building a Resilient City: Managing Natural Disasters. International
Journal of Technology, Volume 9(5), pp. 862–865
BNPB, 2016. Risiko Bencana
Indonesia (Indonesia’s Disaster Risk). Available Online at
http://inarisk.bnpb.go.id/pdf/Buku RBI_Final_low.pdf
Carter, W.N., 2008. Disaster
Management: A Disaster Manager’s Handbook. Philippines: Asian Development Bank
Granville, F., Mehta, A.,
Pike, S., 2016. Destinations, Disasters and Public Relations: Stakeholder
Engagement in Multi-phase Disaster Management. Journal of Hospitality and
Tourism Management, Volume 28, pp. 73–79
Hartono, D.M., Novita, E.,
Gusniani, I., Oriza, I.I.D., 2010. The
Role of Water Supply and Sanitation during Floods: Case Study of Flood Disaster
in Five Regions of Jakarta. International Journal of Technology, Volume
1(1), pp 29–37
Herrmann, J., 2007. Disaster
Response Planning & Preparedness: Phases of Disaster: NYDIS Manual for New
York City Religious Leaders: Spiritual Care and Mental Health for Disaster
Response and Recovery. New York Disaster Interfaith Services (Affiliate of
the National Disaster Interfaiths Network), New York, pp. 11–14
Matos, V., Blake, B., 2006. ‘I
Am OK’: A Conceptual Model for a Global Emergency System and Its Societal
Impact. The International Journal of Technology, Knowledge, and Society,
Volume 2(5), pp. 7–18
Öztaysi, B., Behret, H.,
Kabak, Ö, Uçal Sar?, I., Kahraman, C., 2013. Fuzzy Inference Systems for
Disaster Response, Volume 7, pp. 75–94
Qosasi, A., Maulina, E.,
Purnomo, M., Muftiadi, A., Permana, E., Febrian, F., 2019. The Impact of
Information and Communication Technology Capability on the Competitive Advantage
of Small Businesses. International Journal of Technology, Volume
10(1), pp. 167–177
Satriano, C., Elia, L.,
Martino, C., Lancieri, M., Zollo, A., Iannaccone, G., 2011. PRESTo, the
Earthquake Early Warning System for Southern Italy: Concepts, Capabilities and
Future Perspectives. Soil Dynamics and Earthquake Engineering, Volume
31(2), pp. 137–153
Schweier, C., Markus, M.,
2004. Assessment of the Search and Rescue Demand for Individual Buildings. In: Proceedings of the 13th World Conference on Earthquake
Engineering. Paper No. 3092
Shoushtary, M.A., 2013. Effect
of Information Communication Technology on Human Resources Productivity of the
Iranian National Oil Company. International Journal of Technology, Volume
4(1), pp. 56–62
Tang, J., Zhu, K., Guo, H.,
Liao, C., Zhang, S., 2017. Simulation
Optimization of Search and Rescue in Disaster Relief based on Distributed
Auction Mechanism. Algorithms, Volume 10(4), pp. 1–17
Wu, C.K., 2018. A Game Theory
Approach for Assessing Risk Value and Deploying Search-and-Rescue Resources
after Devastating Tsunamis. Environmental Research, Volume
162, pp. 18–26