IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i1d10.1007_s11269-016-1538-9.html
   My bibliography  Save this article

The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method

Author

Listed:
  • Wen-chuan Wang

    (North China University of Water Resources and Electric Power)

  • Kwok-wing Chau

    (Hong Kong Polytechnic University, Hung Hom)

  • Dong-mei Xu

    (North China University of Water Resources and Electric Power)

  • Lin Qiu

    (North China University of Water Resources and Electric Power)

  • Can-can Liu

    (University College London)

Abstract

Accurate prediction of extreme flood peak discharge is essential in developing the best management practices to avoid and reduce flood disaster. In recent years, many techniques have been pronounced as a branch of computer science to model wide range of hydrological process. Nevertheless, exploration of more efficient technique is necessary in terms of accuracy and applicability. In this study, a novel hermite-PPR model with SSO and LS algorithm is proposed for designing annual maximum flood peak discharge forecasting model at Yichang station on Yangtze River in China. The statistical properties of the data series are utilized for identifying an appropriate input vector to the model and then the performance of the proposed models were compared with adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) methods in terms of root mean squared error (RMSE), mean absolute relative error (MARE), coefficient of correlation (CC), Nash-Sutcliffe efficiency coefficient (NSEC) and qualified rate (QR). The results indicate that the presented methodology in this research can obtain significant improvement in forecasting accuracy in terms of different evaluation criteria during training and validation phases.

Suggested Citation

  • Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Lin Qiu & Can-can Liu, 2017. "The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 461-477, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1538-9
    DOI: 10.1007/s11269-016-1538-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1538-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-016-1538-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Wen-chuan Wang & Dong-mei Xu & Kwok-wing Chau & Guan-jun Lei, 2014. "Assessment of River Water Quality Based on Theory of Variable Fuzzy Sets and Fuzzy Binary Comparison Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4183-4200, September.
    3. Zaw Latt & Hartmut Wittenberg, 2014. "Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2109-2128, June.
    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. Wang, Qiang & Zhan, Lina, 2019. "Assessing the sustainability of the shale gas industry by combining DPSIRM model and RAGA-PP techniques: An empirical analysis of Sichuan and Chongqing, China," Energy, Elsevier, vol. 176(C), pages 353-364.
    2. Hyungjun Park & Robert Paterson & Stephen Zigmund & Hyunsuk Shin & Youngsu Jang & Juchul Jung, 2020. "The Effect of Coastal City Development on Flood Damage in South Korea," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
    3. Junfei Chen & Qian Li & Huimin Wang & Menghua Deng, 2019. "A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China," IJERPH, MDPI, vol. 17(1), pages 1-21, December.
    4. Yu, Xiaohong & Xu, Haiyan & Lou, Wengao & Xu, Xun & Shi, Victor, 2023. "Examining energy eco-efficiency in China's logistics industry," International Journal of Production Economics, Elsevier, vol. 258(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. Fang-Fang Li & Zhi-Yu Wang & Xiao Zhao & En Xie & Jun Qiu, 2019. "Decomposition-ANN Methods for Long-Term Discharge Prediction Based on Fisher’s Ordered Clustering with MESA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3095-3110, July.
    2. Peng Zhang & Xiangsu Chen & Chaohai Fan, 2020. "Research on a Safety Assessment Method for Leakage in a Heavy Oil Gathering Pipeline," Energies, MDPI, vol. 13(6), pages 1-19, March.
    3. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    4. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    5. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    6. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    7. Vidhi Vig & Anmol Kaur, 2022. "Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2920-2933, December.
    8. Baoying Shan & Ping Guo & Shanshan Guo & Zhong Li, 2019. "A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    9. Liangxu Liu & Xueyong Zhao & Qinglan Meng & He Zhao & Xiaoqian Lu & Junkai Gao & Xueli Chang, 2017. "Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
    10. Ali Danandeh Mehr & Vahid Nourani, 2018. "Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2665-2679, June.
    11. Fu Qiao, 2020. "Study on Price Fluctuation of Industry Index in Chinas Stock Market Based on Empirical Mode Decomposition," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(5), pages 559-573, May.
    12. Iván P. Vizcaíno & Enrique V. Carrera & Sergio Muñoz-Romero & Luis H. Cumbal & José Luis Rojo-Álvarez, 2018. "Spatio-Temporal River Contamination Measurements with Electrochemical Probes and Mobile Sensor Networks," Sustainability, MDPI, vol. 10(5), pages 1-27, May.
    13. Parisa-Sadat Ashofteh & Taher Rajaee & Parvin Golfam, 2017. "Assessment of Water Resources Development Projects under Conditions of Climate Change Using Efficiency Indexes (EIs)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3723-3744, September.
    14. Ervin Shan Khai Tiu & Yuk Feng Huang & Jing Lin Ng & Nouar AlDahoul & Ali Najah Ahmed & Ahmed Elshafie, 2022. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 121-153, January.
    15. Zhong-kai Feng & Wen-jing Niu & Zhi-qiang Jiang & Hui Qin & Zhen-guo Song, 2020. "Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2029-2041, April.
    16. Fan, Xinying, 2022. "A method for the generation of typical meteorological year data using ensemble empirical mode decomposition for different climates of China and performance comparison analysis," Energy, Elsevier, vol. 240(C).
    17. Dagnachew Adugna & Marina Bergen Jensen & Brook Lemma & Geremew Sahilu Gebrie, 2018. "Assessing the Potential for Rooftop Rainwater Harvesting from Large Public Institutions," IJERPH, MDPI, vol. 15(2), pages 1-11, February.
    18. Zong-chang Yang, 2018. "Predictive Modeling of Hourly Water-Level Fluctuations Based on the DCT Least-Squares Extended Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1117-1131, February.
    19. Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
    20. Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.

    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:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1538-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.