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| A Imputation Method Based on Improved ANFIS for Environmental Data |
| Received:April 12, 2016 Revised:November 09, 2016 |
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| DOI：10.7643/ issn.1672-9242.2016.06.014 |
| KeyWord:atmospheric corrosion missing data imputation Relevance Factors ANFIS |
| Author | Institution |
| 石雅楠 |
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| 付冬梅 |
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| 支元杰 |
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| 陈闽东 |
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| Abstract: |
| Objective A new method based on Relevance Factors and Adaptive Neuro-Fuzzy Inference System was proposed to impute missing important environmental data of atmospheric corrosion. Methods Through Relevance Factors, the relevance degree between missing data and a number of environmental factors was calculated, selecting valuable factors with high relevance?coefficient, then the relationship model between missing data and chosen factors could be built through ANFIS. Finally taking the imputation of missing SO2 data as the object, the RF-ANFIS model was tested by atmospheric data of Beijing in 2015. Results The RF-ANFIS model were much better than BP in?sample?distribution and error evaluation, which was feasible in finite sample analysis. Conclusion The new method efficiently improves the accuracy of imputing environmental data in atmospheric corrosion, which is vital to predict atmospheric corrosion rate with missing data. |
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