A Combination Model for Displacement Interval Prediction of Concrete Dams Based on Residual Estimation

Xin Yang, Yan Xiang (), Guangze Shen and Meng Sun
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Xin Yang: Nanjing Hydraulic Research Institute, Nanjing 210029, China
Yan Xiang: Nanjing Hydraulic Research Institute, Nanjing 210029, China
Guangze Shen: Nanjing Hydraulic Research Institute, Nanjing 210029, China
Meng Sun: Jiangsu Estuary Waterway for Huaihe River Project Management Office, Huai’an 223000, China

Sustainability, 2022, vol. 14, issue 23, 1-17

Abstract: Accurate prediction and reasonable warning for dam displacement are important contents of dam safety monitoring. However, it is difficult to identify abnormal displacement based on deterministic point prediction results. In response, this paper proposes a model that integrates several strategies to achieve high-precision point prediction and interval prediction of dam displacement. Specifically, the interval prediction of dam displacement is realized in three stages. In the first stage, a displacement prediction model based on Extreme gradient boosting (XGBoost) is constructed. In the second stage, the prediction error sequence of XGBoost model is generated by the residual estimation method proposed in this paper, and the residual prediction model based on artificial neural network (ANN) is constructed through the maximum likelihood estimation method. In the third stage, the interval estimation of the noise sequence composed of the training error of the ANN model is carried out. Finally, the results obtained above are combined to realize the interval prediction of the dam displacement. The performance of the proposed model is verified by the monitoring data of an actual concrete dam. The results show that the hybrid model can not only achieve better point prediction accuracy than the single model, but also provide high quality interval prediction results.

Keywords: displacement prediction model; interval prediction; residual estimation; Extreme gradient boosting; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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