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Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques

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Abstract

Population increase and climate change are stretching not only the world’s but also Pakistan’s water resources. This has directly been responsible for the recurring patterns of floods and droughts in the country which emphasizes the importance of the fact that efficient practices need to be adopted for water resource sustainability. This study investigates the use of upland catchment information, comprising of hydrometeorological datasets for inflow prediction to the Tarbela reservoir (a multipurpose reservoir located on River Indus) using Artificial Neural Networks (ANN) and Regression Techniques (Standard and Step Wise). Input Combination and data length selection for all the selected techniques were performed with the aid of Gamma test (GT). This study has made a significant contribution for future water resource management within the Indus Basin as Tarbela is the main source of irrigation, water supply and hydropower generation in Pakistan along with flood control.

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Acknowledgments

The authors would like to acknowledge the support provided by Water and Power Development Authority (WAPDA) as well as Pakistan Meteorological Department (PMD) Government of Pakistan, in providing the requisite data for this research.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan

    Muhammad Hassan, Muhammad Ali Shamim, Hashim Nisar Hashmi, Ghufran Ahmed Pasha, Usman Ali Naeem & Abdul Razzaq Ghumman

  2. Department of Civil Engineering, Mirpur University of Science and Technology, Mirpur, AK, Pakistan

    Syed Zishan Ashiq & Imtiaz Ahmed

  3. Department of Civil Engineering, University of Bristol, Bristol, UK

    Dawei Han

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  1. Muhammad Hassan
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  2. Muhammad Ali Shamim
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Correspondence to Muhammad Ali Shamim.

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Communicated by: H. A. Babaie

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Hassan, M., Shamim, M.A., Hashmi, H.N. et al. Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques. Earth Sci Inform 8, 337–352 (2015). https://doi.org/10.1007/s12145-014-0161-7

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  • DOI: https://doi.org/10.1007/s12145-014-0161-7

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