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     Research Journal of Applied Sciences, Engineering and Technology


A Fast Environmental Pollution Emergency Plan Generation System Integrating Various Methods

Lifeng Yang and Ziming Kou
Taiyuan University of Technology, Taiyuan 030024, China
Research Journal of Applied Sciences, Engineering and Technology  2013  10:2897-2901
http://dx.doi.org/10.19026/rjaset.5.4596  |  © The Author(s) 2013
Received: August 17, 2012  |  Accepted: September 13, 2012  |  Published: March 25, 2013

Abstract

Integrating methods and techniques of CBR, RBR, R-AHP and GIS etc., this study designed and developed a fast environmental pollution emergency plan generation system. The study analyzes the working principle and process that generate emergency plan combining CBR and RBR. In CBR system, R-AHP is used to construct the case index system of environmental pollution emergencies and calculate the weight of index; similarity between cases is measured by nearest-neighbour algorithm. Rule-based reasoning is performed in accordance with the rules in the knowledge base and GIS is used to achieve the visual expression of the dynamic emergency plan. Integration of various methods optimizes the response time and complexity of system, also enhances the practical operability. This study has great significance in researching of environmental emergency management and improving emergency response capability.

Keywords:

CBR, environmental pollution, emergency plan, GIS, RBR, R-AHP,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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