A Fuzzy Method for Assessing Eco-Environmental Disaster Risk Caused by Coalbed Methane in China
- DOI
- 10.2991/jrarc.2018.8.1.1How to use a DOI?
- Keywords
- Coalbed methane; eco-environment; risk assessment; intuitionistic fuzzy set; fuzzy neural system
- Abstract
Based on an improved risk assessment index system, this paper constructs an eco-environmental disaster risk assessment model during coalbed methane industrialization development in China by using intuitionistic fuzzy sets to describe the uncertain risk information, and the transformed interval value of Mamdani intuitionistic fuzzy neural networks model. Then, the validity of the model is verified by simulation tests. Furthermore, the assessment results are compared with those obtained by fuzzy neural networks model. Results show that suggested model has multidimensional nonlinearity and global approximation characteristics. By the procedure of “fuzzification - fuzzy-rules - defuzzifier”, the output conversion from uncertainty quantitative indicators to accurate risk assessment values can be effectively realized. Compared with the fuzzy neural networks model, the suggested model has better accuracy and stability. The risk assessment value calculated by the suggested model fairly matches the expected one. The study supplies a decision support for routine supervision and risk precaution and management on one hand, and enriches the theoretical research of the eco-environmental risk assessment of coalbed methane industrialization development on the other hand.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Ye Xue AU - Xiaoxiao Li AU - Wen Sun AU - Baozhang Chen PY - 2018 DA - 2018/03/31 TI - A Fuzzy Method for Assessing Eco-Environmental Disaster Risk Caused by Coalbed Methane in China JO - Journal of Risk Analysis and Crisis Response SP - 3 EP - 13 VL - 8 IS - 1 SN - 2210-8505 UR - https://doi.org/10.2991/jrarc.2018.8.1.1 DO - 10.2991/jrarc.2018.8.1.1 ID - Xue2018 ER -