Yang, M.Z.; Wang, Y.X.; Wu, J.; Song, J.J.; Gao, Y.; Wang, Z.G.; Lan, S.H.; Gan, F.B. General evaluation model for complex environment system. Preprints2023, 2023070299. https://doi.org/10.20944/preprints202307.0299.v1
APA Style
Yang, M.Z., Wang, Y.X., Wu, J., Song, J.J., Gao, Y., Wang, Z.G., Lan, S.H., & Gan, F.B. (2023). General evaluation model for complex environment system. Preprints. https://doi.org/10.20944/preprints202307.0299.v1
Chicago/Turabian Style
Yang, M.Z., Shi Hao Lan and Fu Bao Gan. 2023 "General evaluation model for complex environment system" Preprints. https://doi.org/10.20944/preprints202307.0299.v1
Abstract
Establishing an effective evaluation model to analyze the actual environmental level of the multi-indicator systems is an urgent challenge. However, due to the complexity of the environmental system, the factors that determine the practical system are usually interrelated. To solve this problem, many methods have been proposed and verified. However, the disadvantage of unreasonable weight determination and only single-indicator assessment limits the practical application of these analysis methods. Here, we established an optimized entropy weight model and integrate it with the fuzzy comprehensive evaluation (FCE) method to quantify the complex environmental system. By introducing the linear programming method into the entropy weight solution, we can obtain high accuracy weight values and strong resistance to extreme data. Theoretical and simulation results demonstrate that our method can enhance the precision of weight calculation and evaluation results for multi-factor systems. Our work develops an effective method to quantify the complex environment system comprehensively and is significant to real applications of evaluation method.
Keywords
Optimized improved entropy evaluation; Fuzzy comprehensive evaluation; Practical environmental system assessment; Linear programming.
Subject
Physical Sciences, Applied Physics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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