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Deformation Prediction of Dam Based on Optimized Grey Verhulst Model

Author

Listed:
  • Changjun Huang

    (School of Municipal and Surveying Engineering, Hunan City University, Yiyang 413000, China)

  • Lv Zhou

    (School of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China)

  • Fenliang Liu

    (School of Municipal and Surveying Engineering, Hunan City University, Yiyang 413000, China)

  • Yuanzhi Cao

    (School of Municipal and Surveying Engineering, Hunan City University, Yiyang 413000, China)

  • Zhong Liu

    (Hunan Remote Sensing Geological Survey and Monitoring Institute, Changsha 411000, China)

  • Yun Xue

    (School of Municipal and Surveying Engineering, Hunan City University, Yiyang 413000, China)

Abstract

Dam deformation monitoring data are generally characterized by non-smooth and no-saturated S-type fluctuation. The grey Verhulst model can get better results only when the data series is non-monotonic swing development and the saturated S-shaped sequence. Due to the limitations of the grey Verhulst model, the prediction accuracy will be limited to a certain extent. Aiming at the shortages in the prediction based on the traditional Verhulst model, the optimized grey Verhulst model is proposed to improve the prediction accuracy of the dam deformation monitoring. Compared with those of the traditional GM (1,1) model, the DGM (2,1) model, and the traditional Verhulst (1,1) model, the experimental results show that the new proposed optimized Verhulst model has higher prediction accuracy than the traditional gray model. This study offers an effective model for dealing with the non-saturated fluctuation sequence to predict dam deformation under uncertain conditions.

Suggested Citation

  • Changjun Huang & Lv Zhou & Fenliang Liu & Yuanzhi Cao & Zhong Liu & Yun Xue, 2023. "Deformation Prediction of Dam Based on Optimized Grey Verhulst Model," Mathematics, MDPI, vol. 11(7), pages 1-15, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1729-:d:1115961
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    References listed on IDEAS

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    1. Erinc Karatoprak & Serhat Seker, 2019. "An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-9, February.
    2. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).
    3. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    4. Ning-bo Zhao & Jia-long Yang & Shu-ying Li & Yue-wu Sun, 2014. "A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, April.
    5. Louvrier, Julie & Chambert, Thierry & Marboutin, Eric & Gimenez, Olivier, 2018. "Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models," Ecological Modelling, Elsevier, vol. 387(C), pages 61-69.
    6. Amevi Acakpovi & Alfred Tettey Ternor & Nana Yaw Asabere & Patrick Adjei & Abdul-Shakud Iddrisu, 2020. "Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, August.
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