|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: planning, mitigation, response, recovery, and evaluation (Herrmann, 2007). The response phase is the execution phase of the planning; it addresses the need to reduce the losses caused by the disaster. Therefore, all stakeholders must contribute to increasing the efficiency of the disaster response.
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.
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