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:: Volume 11, Issue 2 (12-2021) ::
JGST 2021, 11(2): 153-162 Back to browse issues page
Designing a Context-aware Recommender System in the Optimization of the Relief and Rescue by Ant Colony Optimization Algorithm and Geospatial Information System
N. Bahrami, M. Argany *, N. Neysani Samani, A. R. Vafaei Nejad
Abstract:   (444 Views)
One of the significant dangers to human life is crises and natural disasters worldwide every year. If such incidents are unpredictable, their risks and casualties will be much higher. Among disasters are floods, hurricanes, volcanoes, earthquakes, tsunamis. An earthquake is an event that is more prevalent than other disasters and is almost unpredictable. Respond structure to crises and disasters is called crisis management which deals with all issues before, during, and after crises and disasters and leads to activities in the field of planning, preparedness, prevention, response, and reconstruction.
One of the most basic and essential things that can reduce the casualties of various events is disaster relief and rescue, which respond to the crisis management structure. Also, the contextual information of the environment, rescuers, and activities created a context-aware recommending system that can facilitate the process of interaction with the environment. This study has checked the types of contexts, their relationship, and the structure of earthquake rescue in Iran, where there is a significant crisis due to geographical location and seismicity.
The whole problem space consists of three parts to provide a meaningful definition of the concept of "context" in the deployment of relief and rescue teams. The rescuer is the main context. Relief and rescue team as the object's environment, which includes team information of rescuers, consists of team members' position, distance, physical condition, and activity compared to other rescuers in the group. The physical environment is a collection of injured people, buildings, and relief and rescue teams in a specific area.
Contexts of rescuers and their relationships, teams, and the hypothetical earthquake were studied by studying related articles and books and interviewing experts in the field of research. The study of context-aware and optimization methods used for the actual structure of rescue teams are the innovations of this study. Contexts during relief and rescue include location, time, the extent of human and building injuries, rescuers' interactions with and with the environment, and activities, rescuers' specialties, and priorities. Interaction is necessary for optimal management of relief and rescue. To create a relationship between the various contexts and optimize the relief and rescue process by defining the mathematical function and using sensed information from contexts into the proposed optimized algorithm.
Finally, the solution has been designed and implemented with an ant colony algorithm and geospatial information system to optimize the allocation of rescuers to the affected areas and the necessary activities in the part of Tehran. The use of combination context-aware and artificial intelligence algorithms for the subject of relief and rescue in earthquake crisis is new research that Led to a 1.79-fold improvement of the proposed solution compared to not considering the existing contexts in relief and rescue without using artificial intelligence algorithms.
So can be created a context-aware system based on the appropriate optimization algorithm as a suitable solution to the problem of post-earthquake relief and rescue. Due to the context structure in this research in the individual's activity effectiveness on other individuals and groups, the ant colony algorithm is a collective intelligence base that can provide optimal positioning in a discrete environment. It allows more repetition in less time than other algorithms.
Article number: 10
Keywords: Context-aware, Optimization, Relief & Rescue, Ant Colony Optimization, Geospatial Information System, Earthquake
Full-Text [PDF 1031 kb]   (159 Downloads)    
Type of Study: Research | Subject: GIS
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Bahrami N, Argany M, Neysani Samani N, Vafaei Nejad A R. Designing a Context-aware Recommender System in the Optimization of the Relief and Rescue by Ant Colony Optimization Algorithm and Geospatial Information System. JGST. 2021; 11 (2) :153-162
URL: http://jgst.issge.ir/article-1-1006-en.html


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Volume 11, Issue 2 (12-2021) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology