IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i3d10.1007_s11269-016-1556-7.html
   My bibliography  Save this article

Performance Enhancement of Rainfall Pattern – Water Level Prediction Model Utilizing Self-Organizing-Map Clustering Method

Author

Listed:
  • Fahimi Farzad

    (University Kebangsaan Malaysia)

  • Ahmed H. El-Shafie

    (University Malaya)

Abstract

During recent two decades, Artificial Neural Network (ANN) has become one of the most widely used methods in hydrology. One solution for better capturing the existing non-linear and complex nature of data is to develop new hybrid approaches. These hybrid models can be developed in a way that two or more techniques are combined in order to benefit from the advantages of these available approaches and eliminate their limitations. The main scope of this paper is to improve the performance of rainfall-water level modeling by combining ANN with Self Organizing Map (SOM) as an unsupervised clustering method. The proposed method in this study consists of two phases. In the first phase, with the aim of reducing the complexity and dimensionality of input data, a two-step clustering using SOM technique is carried out. Then, in the second phase, separate ANN models are used to model each cluster of data, and final results are obtained by combining the outputs of all models. The proposed new hybrid approach is evaluated using real hydrological data of Johor River. The results of the study indicate that the new proposed SOM-ANN hybrid model has a better performance in daily rainfall-water level forecasting compared to ANN model alone.

Suggested Citation

  • Fahimi Farzad & Ahmed H. El-Shafie, 2017. "Performance Enhancement of Rainfall Pattern – Water Level Prediction Model Utilizing Self-Organizing-Map Clustering Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 945-959, February.
  • Handle: RePEc:spr:waterr:v:31:y:2017:i:3:d:10.1007_s11269-016-1556-7
    DOI: 10.1007/s11269-016-1556-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1556-7
    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/s11269-016-1556-7?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. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    2. Mohammed Falah Allawi & Ahmed El-Shafie, 2016. "Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4773-4788, October.
    3. Zaw Latt & Hartmut Wittenberg & Brigitte Urban, 2015. "Clustering Hydrological Homogeneous Regions and Neural Network Based Index Flood Estimation for Ungauged Catchments: an Example of the Chindwin River in Myanmar," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(3), pages 913-928, February.
    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. Renee Obringer & Dave D. White, 2023. "Leveraging Unsupervised Learning to Develop a Typology of Residential Water Users’ Attitudes Towards Conservation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 37-53, 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. Isabel Kaufmann Almeida & Aleska Kaufmann Almeida & Jorge Luiz Steffen & Teodorico Alves Sobrinho, 2016. "Model for Estimating the Time of Concentration in Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4083-4096, September.
    2. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
    3. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    4. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.
    5. A. Agarwal & R. Maheswaran & J Kurths & R. Khosa, 2016. "Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization -a Case Study in the Western United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4399-4413, September.
    6. Tao Guo & Wei He & Zhonglian Jiang & Xiumin Chu & Reza Malekian & Zhixiong Li, 2018. "An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level," Energies, MDPI, vol. 12(1), pages 1-11, December.
    7. Meral Buyukyildiz & Gulay Tezel & Volkan Yilmaz, 2014. "Estimation of the Change in Lake Water Level by Artificial Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4747-4763, October.
    8. Ino Papageorgaki & Ioannis Nalbantis, 2016. "Classification of Drainage Basins Based on Readily Available Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5559-5574, December.
    9. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    10. Jalal Shiri & Shahaboddin Shamshirband & Ozgur Kisi & Sepideh Karimi & Seyyed M Bateni & Seyed Hossein Hosseini Nezhad & Arsalan Hashemi, 2016. "Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5217-5229, November.
    11. Xinxin He & Jungang Luo & Peng Li & Ganggang Zuo & Jiancang Xie, 2020. "A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 865-884, January.

    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:3:d:10.1007_s11269-016-1556-7. 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.