IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i7p1729-d1115961.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/7/1729/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/7/1729/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Zhiwen & Zhao, Ming & Lv, Yi & Wang, Iwei & Tariq, Ghulam & Zhao, Sheng & Ahmed, Shakil & Dong, Weiguo & Ji, Guozhao, 2024. "Higher heating value prediction of high ash gasification-residues: Comparison of white, grey, and black box models," Energy, Elsevier, vol. 288(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yadong & Wang, Delu & Shi, Xunpeng, 2023. "Sustainable development pathways of China's wind power industry under uncertainties: Perspective from economic benefits and technical potential," Energy Policy, Elsevier, vol. 182(C).
    2. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    3. Faisal Mumtaz & Kashif Imran & Abdullah Abusorrah & Syed Basit Ali Bukhari, 2022. "Harmonic Content-Based Protection Method for Microgrids via 1-Dimensional Recursive Median Filtering Algorithm," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    4. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    5. Lifang Zhang & Jianzhou Wang & Zhenkun Liu, 2023. "Power grid operation optimization and forecasting using a combined forecasting system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 124-153, January.
    6. Janusz Sowinski, 2021. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company," Energies, MDPI, vol. 14(2), pages 1-18, January.
    7. Zhang, Haipeng & Wang, Jianzhou & Qian, Yuansheng & Li, Qiwei, 2024. "Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM," Energy, Elsevier, vol. 294(C).
    8. Ahmed Nazmus Sakib & Talayeh Razzaghi & Md Monjur Hossain Bhuiyan, 2023. "Forecasting the Fuel Consumption and Price for a Future Pandemic Outbreak: A Case Study in the USA under COVID-19," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    9. Teresa Castiglione & Diego Perrone & Luciano Strafella & Antonio Ficarella & Sergio Bova, 2023. "Linear Model of a Turboshaft Aero-Engine Including Components Degradation for Control-Oriented Applications," Energies, MDPI, vol. 16(6), pages 1-18, March.
    10. Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
    11. Weiguo Dong & Zhiwen Chen & Jiacong Chen & Zhao Jia Ting & Rui Zhang & Guozhao Ji & Ming Zhao, 2022. "A Novel Method for the Estimation of Higher Heating Value of Municipal Solid Wastes," Energies, MDPI, vol. 15(7), pages 1-14, April.
    12. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    13. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    14. Sahar Ahmadzadeh & Tahmina Ajmal & Ramakrishnan Ramanathan & Yanqing Duan, 2023. "A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    15. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    16. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    17. Zhang, Yonghong & Mao, Shuhua & Kang, Yuxiao & Wen, Jianghui, 2021. "Fractal derivative fractional grey Riccati model and its application," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    18. Pedro M. R. Bento & Jose A. N. Pombo & Maria R. A. Calado & Silvio J. P. S. Mariano, 2021. "Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting," Energies, MDPI, vol. 14(21), pages 1-21, November.
    19. Leoni, Leonardo & De Carlo, Filippo & Abaei, Mohammad Mahdi & BahooToroody, Ahmad & Tucci, Mario, 2023. "Failure diagnosis of a compressor subjected to surge events: A data-driven framework," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    20. Xiaohui Gao, 2022. "Monthly Wind Power Forecasting: Integrated Model Based on Grey Model and Machine Learning," Sustainability, MDPI, vol. 14(22), pages 1-14, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1729-:d:1115961. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.