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Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks

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  • R. Gopakumar
  • Kaoru Takara
  • E. James

Abstract

The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models. Copyright Springer Science+Business Media, Inc. 2007

Suggested Citation

  • R. Gopakumar & Kaoru Takara & E. James, 2007. "Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(11), pages 1915-1940, November.
    • Handle: RePEc:spr:waterr:v:21:y:2007:i:11:p:1915-1940
    DOI: 10.1007/s11269-006-9137-9
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    References listed on IDEAS

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    1. Bolton, Roger E. & Jackson, Randall W. & West, Guy R., 1989. "Introduction," Socio-Economic Planning Sciences, Elsevier, vol. 23(5), pages 237-240.
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    Cited by:

    1. Mohammed Seyam & Faridah Othman, 2014. "The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2583-2597, July.
    2. Naser Dehghanian & S. Saeid Mousavi Nadoushani & Bahram Saghafian & Ruhangiz Akhtari, 2019. "Performance Evaluation of a Fuzzy Hybrid Clustering Technique to Identify Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4621-4636, October.
    3. Mehdi Rezaeian Zadeh & Seifollah Amin & Davar Khalili & Vijay Singh, 2010. "Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2673-2688, September.
    4. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    5. Raymond Kim & Daniel Loucks & Jery Stedinger, 2012. "Artificial Neural Network Models of Watershed Nutrient Loading," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2781-2797, August.
    6. Desalegn Edossa & Mukand Babel, 2011. "Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1759-1773, April.

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