International Journal of Nonlinear Analysis and Applications

A fuzzy inference system for predicting relief goods demand in the different scenarios of occurrence earthquake

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran

Abstract

Earthquake is one of the natural disasters that, depending on the scale, location, preventive measures, etc, can have financial and human effects to a large extent. Natural disasters have an uncertain nature. In other words, regarding the statistics related to such events, it is not possible to comment with high accuracy, which reveals the need to predict and estimate the dimensions of the possible effects of natural disasters. It should be noted that predicting the demand for relief goods and estimating the number of injured and displaced people as a result of these disasters can increase the efficiency of rescue operations and reduce the duration of this process, resulting in more services and reducing casualties and costs. According to the mentioned cases and the necessity of conducting effective research from different aspects and in different fields, especially in the field of predicting and management of relief and disasters, this research was carried out. For this purpose, factors such as population ratios such as population density, texture erosion in different areas, earthquake time and earthquake intensity based on the Richter scale, taking into consideration the opinions of experts in the field of geology and relief rescue, were used as inputs to the fuzzy inference system and the output of this system is determined by items such as the demand for biological, food, pharmaceutical goods, as well as the number of displaced and injured people.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 2
July 2022
Pages 651-671
  • Receive Date: 08 February 2022
  • Revise Date: 04 March 2022
  • Accept Date: 18 June 2022