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Artificial neural network model for synthetic streamflow generation

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  • Juran Ahmed
  • Arup Sarma

Abstract

Time series of streamflow plays an important role in planning, design and management of water resources system. In the event of non availability of a long series of historical streamflow record, generation of the data series is of utmost importance. Although a number of models exist, they may not always produce satisfactory result in respect of statistics of the historical data. In such event, artificial neural network (ANN) model can be a potential alternative to the conventional models. Streamflow series, which is a stochastic phenomenon, can be suitably modeled by ANN for its strong capability to perform non-linear mapping. An ANN model developed for generating synthetic streamflow series of the Pagladia River, a major north bank tributary of the river Brahmaputra, is presented in this paper along with its comparison with other existing models. The comparison carried out in respect of five different statistics of the historical data and synthetically generated data has shown that among the different models, viz., autoregressive moving average (ARMA) model, Thomas-Fiering model and ANN model, the ANN based model has performed better in generating synthetic streamflow series for the Pagladia River. Copyright Springer Science + Business Media B.V. 2007

Suggested Citation

  • Juran Ahmed & Arup Sarma, 2007. "Artificial neural network model for synthetic streamflow generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 1015-1029, June.
    • Handle: RePEc:spr:waterr:v:21:y:2007:i:6:p:1015-1029
    DOI: 10.1007/s11269-006-9070-y
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    Cited by:

    1. Zaher Mundher Yaseen & Minglei Fu & Chen Wang & Wan Hanna Melini Wan Mohtar & Ravinesh C. Deo & Ahmed El-shafie, 2018. "Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1883-1899, March.
    2. Đurica Marković & Jasna Plavšić & Nesa Ilich & Siniša Ilić, 2015. "Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4787-4801, October.
    3. Maria Manuela Portela & Artur Tiago Silva, 2016. "Disaggregation Modelling of Annual Flows into Daily Streamflows Using a New Approach of the Method of Fragments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5589-5607, December.
    4. Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2008. "Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(6), pages 757-774, June.
    5. Maya Rajnarayan Ray & Arup Kumar Sarma, 2016. "Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4695-4711, October.
    6. Yi-min Wang & Jian-xia Chang & Qiang Huang, 2010. "Simulation with RBF Neural Network Model for Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2597-2610, September.
    7. Saman Razavi & Shahab Araghinejad, 2009. "Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(1), pages 39-55, January.
    8. 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.
    9. Roy Setiawan & Reza Daneshfar & Omid Rezvanjou & Siavash Ashoori & Maryam Naseri, 2021. "Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17606-17627, December.
    10. Rajib Bhattacharjya & Sandeep Chaurasia, 2013. "Geomorphology Based Semi-Distributed Approach for Modelling Rainfall-Runoff Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 567-579, January.
    11. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    12. Veysel Güldal & Hakan Tongal, 2010. "Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 105-128, January.
    13. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.
    14. Yonas Ghile & Roland Schulze, 2010. "Evaluation of Three Numerical Weather Prediction Models for Short and Medium Range Agrohydrological Applications," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 1005-1028, March.
    15. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.

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