A Novel Outlier Detection Method of Long-Term Dam Monitoring Data Based on SSA-NAR

Outlier generally exists in dam monitoring data which may seriously affect the accuracy of dam safety evaluation results. Aiming at the outlier detection of dam monitoring data, a novel dynamic detection method of dam outlier data based on SSA-NAR is proposed. This combined method does not depend on...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-08, Vol.2022, p.1-11
Main Authors: Song, Jintao, Chen, Yongchao, Yang, Jie
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Dam safety
Data analysis
Evaluation
Expected values
Foraging behavior
Mathematical models
Model accuracy
Monitoring
Monitoring systems
Neural networks
Normal distribution
Optimization algorithms
Outliers (statistics)
Parameter identification
Prediction models
Random variables
Search algorithms
Time series
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Summary:Outlier generally exists in dam monitoring data which may seriously affect the accuracy of dam safety evaluation results. Aiming at the outlier detection of dam monitoring data, a novel dynamic detection method of dam outlier data based on SSA-NAR is proposed. This combined method does not depend on the effect quantity and influence quantity relationship of traditional dam safety theory and only uses the time series of effect quantity to mine the variation, which can avoid the impact of missing or abnormal of the influence quantity. The Nonlinear Autoregression (NAR) is a classical time series neural network widely used in engineering field. However, the prediction accuracy of NAR is greatly affected by the selection of model parameters, the Sparrow Search Algorithm (SSA) which is a novel model parameter solution method and can be combined with NAR to derive the optimal parameters of NAR prediction model. The outlier is identified through the analysis of the residual distribution between the predicted data and the measured data. The case study shows that when the original data does not contain outliers, the prediction accuracy of the model is high. When the outlier is included, the proposed model has good robustness which the outlier has little influence on the prediction effect. It can effectively detect the outlier in the original dam monitoring data and provide a reliable data basis for dam safety evaluation.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/6569367