IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v30y2016i11d10.1007_s11269-016-1409-4.html
   My bibliography  Save this article

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Author

Listed:
  • Youngmin Seo

    (Kyungpook National University)

  • Sungwon Kim

    (Dongyang University)

  • Ozgur Kisi

    (Canik Basari University)

  • Vijay P. Singh

    (Texas A & M University)

  • Kamban Parasuraman

    (AIR Worldwide)

Abstract

This study develops and applies three hybrid models, including wavelet packet-artificial neural network (WPANN), wavelet packet-adaptive neuro-fuzzy inference system (WPANFIS) and wavelet packet-support vector machine (WPSVM), combining wavelet packet decomposition (WPD) and machine learning models, ANN, ANFIS and SVM models, for forecasting daily river stage and evaluates their performance. The WPANN, WPANFIS and WPSVM models using inputs decomposed by the WPD are found to produce higher efficiency based on statistical performance criteria than the ANN, ANFIS and SVM models using original inputs. Performance evaluation for various mother wavelets indicates that the model performance is dependent on mother wavelets and the WPD using Symmlet-10 and Coiflet-18 is more effective to enhance the efficiency of the conventional machine learning models than other mother wavelets. It is found that the WPANFIS model outperforms the WPANN and WPSVM models, and the WPANFIS14-coif18 model produces the best performance among all other models in terms of model efficiency. Therefore, the WPD can significantly enhance the accuracy of the conventional machine learning models, and the conjunction of the WPD and machine learning models can be an effective tool for forecasting daily river stage accurately .

Suggested Citation

  • Youngmin Seo & Sungwon Kim & Ozgur Kisi & Vijay P. Singh & Kamban Parasuraman, 2016. "River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 4011-4035, September.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:11:d:10.1007_s11269-016-1409-4
    DOI: 10.1007/s11269-016-1409-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1409-4
    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/s11269-016-1409-4?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. Horng-I Hsieh & Tsung-Pei Lee & Tian-Shyug Lee, 2011. "A Hybrid Particle Swarm Optimization and Support Vector Regression Model for Financial Time Series Forecasting," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 2(2), pages 48-56, May.
    2. Sungwon Kim & Jalal Shiri & Ozgur Kisi & Vijay Singh, 2013. "Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2267-2286, May.
    3. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    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. Eseosa Halima Ighile & Hiroaki Shirakawa & Hiroki Tanikawa, 2022. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria," Sustainability, MDPI, vol. 14(9), pages 1-33, April.
    2. Mustafa Najat Asaad & Şule Eryürük & Kağan Eryürük, 2022. "Forecasting of Streamflow and Comparison of Artificial Intelligence Methods: A Case Study for Meram Stream in Konya, Turkey," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    3. Adnan Bashir & Muhammad Ahmed Shehzad & Ijaz Hussain & Muhammad Ishaq Asif Rehmani & Sajjad Haider Bhatti, 2019. "Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5121-5136, December.
    4. Jin-Cheng Fu & Hsiao-Yun Huang & Jiun-Huei Jang & Pei-Hsun Huang, 2019. "River Stage Forecasting Using Multiple Additive Regression Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4491-4507, October.
    5. Wen-chuan Wang & Yu-jin Du & Kwok-wing Chau & Dong-mei Xu & Chang-jun Liu & Qiang Ma, 2021. "An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4695-4726, November.
    6. José-Luis Molina & Santiago Zazo & Ana-María Martín-Casado & María-Carmen Patino-Alonso, 2020. "Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.

    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. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    2. Xianming Dou & Yongguo Yang & Jinhui Luo, 2018. "Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements," Sustainability, MDPI, vol. 10(1), pages 1-26, January.
    3. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    4. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    5. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    6. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    7. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    8. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    9. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
    10. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
    11. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    12. Salcedo-Sanz, S. & Pastor-Sánchez, A. & Del Ser, J. & Prieto, L. & Geem, Z.W., 2015. "A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction," Renewable Energy, Elsevier, vol. 75(C), pages 93-101.
    13. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    14. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    15. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
    16. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    17. Hugo Tavares Vieira Gouveia & Ronaldo Ribeiro Barbosa De Aquino & Aida Araújo Ferreira, 2018. "Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks," Energies, MDPI, vol. 11(4), pages 1-19, April.
    18. Wei Sun & Qi Gao, 2019. "Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model," Energies, MDPI, vol. 12(12), pages 1-27, June.
    19. Yang, Qiuling & Deng, Changhong & Chang, Xiqiang, 2022. "Ultra-short-term / short-term wind speed prediction based on improved singular spectrum analysis," Renewable Energy, Elsevier, vol. 184(C), pages 36-44.
    20. Zhenfang He & Yaonan Zhang & Qingchun Guo & Xueru Zhao, 2014. "Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5297-5317, December.

    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:30:y:2016:i:11:d:10.1007_s11269-016-1409-4. 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.