IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v30y2016i1p79-97.html
   My bibliography  Save this article

Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models

Author

Listed:
  • Maryam Shafaei
  • Ozgur Kisi

Abstract

Accurate predicting of lake level fluctuations is essential and basic in water resources management for water supply purposes. The predicting of lake level is complicated because of it is affected by nonlinear hydrological processes. This paper applies integrated wavelet and auto regressive moving average (ARMA), adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) models for forecasting monthly lake level fluctuations. First, lake level time series is decomposed into low and high frequency components by using discrete wavelet transform. Then, each component is separately predicted by using ARMA, ANFIS and SVR models. Finally, the predicted components are summed to obtain estimated original lake level time series. The performance of the proposed WSVR (Wavelet-SVR), WANFIS (Wavelet-ANFIS) and WARMA (Wavelet-ARMA) models is compared with single ARMA, SVR and ANFIS models. Results show that the integrated models give better precision in forecasting lake levels in the study region when compared to single models. WSVR model is found to be slightly better than the other integrated models. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Maryam Shafaei & Ozgur Kisi, 2016. "Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 79-97, January.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:1:p:79-97
    DOI: 10.1007/s11269-015-1147-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-1147-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-015-1147-z?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. Ozgur Kisi & Jalal Shiri, 2011. "Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3135-3152, October.
    2. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    3. Hui-cheng Zhou & Yong Peng & Guo-hua Liang, 2008. "The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(2), pages 217-227, February.
    4. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    5. Alpaslan Yarar, 2014. "A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 553-565, January.
    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. Mustafa Erkan Turan, 2016. "Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4345-4362, September.
    2. Siriporn Supratid & Thannob Aribarg & Seree Supharatid, 2017. "An Integration of Stationary Wavelet Transform and Nonlinear Autoregressive Neural Network with Exogenous Input for Baseline and Future Forecasting of Reservoir Inflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 4023-4043, September.
    3. Guoqiang Chen & Tianyu Long & Jiangong Xiong & Yun Bai, 2017. "Multiple Random Forests Modelling for Urban Water Consumption Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4715-4729, December.
    4. Amir Hossein Zaji & Hossein Bonakdari & Bahram Gharabaghi, 2019. "Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4215-4230, September.
    5. P. Biglarbeigi & W. A. Strong & D. Finlay & R. McDermott & P. Griffiths, 2020. "A Hybrid Model-Based Adaptive Framework for the Analysis of Climate Change Impact on Reservoir Performance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4053-4066, October.
    6. Hossein Bonakdari & Isa Ebtehaj & Pijush Samui & Bahram Gharabaghi, 2019. "Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3965-3984, September.
    7. Xike Zhang & Qiuwen Zhang & Gui Zhang & Zhiping Nie & Zifan Gui & Huafei Que, 2018. "A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition," IJERPH, MDPI, vol. 15(5), pages 1-23, May.
    8. Jinping Zhang & Honglin Xiao & Hongyuan Fang, 2022. "Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 433-448, January.
    9. Pratik Pathak & Ajay Kalra & Sajjad Ahmad & Miguel Bernardez, 2016. "Wavelet-Aided Analysis to Estimate Seasonal Variability and Dominant Periodicities in Temperature, Precipitation, and Streamflow in the Midwestern United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4649-4665, October.
    10. Vahid Moosavi & Ali Talebi & Mohammad Reza Hadian, 2017. "Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 43-59, January.

    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. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    2. Rajeev Sahay & Ayush Srivastava, 2014. "Predicting Monsoon Floods in Rivers Embedding Wavelet Transform, Genetic Algorithm and Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 301-317, January.
    3. Dimitrios Myronidis & Konstantinos Ioannou & Dimitrios Fotakis & Gerald Dörflinger, 2018. "Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1759-1776, March.
    4. Vahid Nourani & Mehdi Komasi & Akira Mano, 2009. "A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 2877-2894, November.
    5. Vinit Sehgal & Rajeev Sahay & Chandranath Chatterjee, 2014. "Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1733-1749, April.
    6. Alpaslan Yarar, 2014. "A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 553-565, January.
    7. Pratik Pathak & Ajay Kalra & Sajjad Ahmad & Miguel Bernardez, 2016. "Wavelet-Aided Analysis to Estimate Seasonal Variability and Dominant Periodicities in Temperature, Precipitation, and Streamflow in the Midwestern United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4649-4665, October.
    8. 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.
    9. Haijiao Yu & Xiaohu Wen & Qi Feng & Ravinesh C. Deo & Jianhua Si & Min Wu, 2018. "Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 301-323, January.
    10. Sabah Saadi Fayaed & Seef Saadi Fiyadh & Wong Jee Khai & Ali Najah Ahmed & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Chow Ming Fai & Suhana Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Ja, 2019. "Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    11. 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.
    12. Konrad Bogner & Katharina Liechti & Luzi Bernhard & Samuel Monhart & Massimiliano Zappa, 2018. "Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 969-984, February.
    13. Wensheng Wang & Juliang Jin & Yueqing Li, 2009. "Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2791-2803, October.
    14. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    15. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2014. "Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 425-444, January.
    16. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    17. Anas Mahmood Al-Juboori, 2021. "A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 729-743, January.
    18. Muhammad Shoaib & Asaad Y. Shamseldin & Sher Khan & Mudasser Muneer Khan & Zahid Mahmood Khan & Tahir Sultan & Bruce W. Melville, 2018. "A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 83-103, January.
    19. Alexandre Evsukoff & Beatriz Lima & Nelson Ebecken, 2011. "Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(3), pages 963-985, February.
    20. E. Fallah-Mehdipour & O. Bozorg Haddad & H. Orouji & M. Mariño, 2013. "Application of Genetic Programming in Stage Hydrograph Routing of Open Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3261-3272, July.

    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:1:p:79-97. 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.