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Developing nonlinear models for sediment load estimation in an irrigation canal

  • Research Article - Hydrology
  • Published:
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Abstract

The study was performed to estimate the weekly sediment load in Thal canal located in Mianwali district Punjab, Pakistan. Past records of sediments and discharge have been considered as the input parameters. The best input combinations have been identified with the help of advanced algorithms including full, sequential and increasing embedding, genetic algorithm and hill climbing in combination with the gamma test. Model training has been carried out using two artificial neural network-based algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), back-propagation and a local linear regression technique. A variety of statistical parameters including R square, root mean squared error, mean square error and mean bias error (MBE) has been calculated in order to evaluate the best models. The results strongly suggested that BFGS-based model performed better than all other models with remarkably low values of MBE. Significantly high values of correlation coefficient (R square) in both training and testing evidenced a close similarity between actual and predicted sediment load values for the same model.

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering and Technology, University of Sargodha, Sargodha, 40100, Pakistan

    Fahad Ahmed

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

    Muhammad Hassan

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

    Hashim Nisar Hashmi

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  1. Fahad Ahmed
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  2. Muhammad Hassan
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  3. Hashim Nisar Hashmi
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Correspondence to Fahad Ahmed.

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Ahmed, F., Hassan, M. & Hashmi, H.N. Developing nonlinear models for sediment load estimation in an irrigation canal. Acta Geophys. 66, 1485–1494 (2018). https://doi.org/10.1007/s11600-018-0221-3

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