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Development of sediment load estimation models by using artificial neural networking techniques

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Abstract

This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions, discharge, and temperature information. Model input and data length selection was carried out using a novel mathematical tool, Gamma test. Model training was carried out by using three popular algorithms namely Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) using forward selection of input variables. Evaluation of the best model was carried out on the basis of basic statistical parameters namely R-square, root mean squared error (RMSE), and mean biased error (MBE). Results indicated that BFGS-based ANN model outperformed all other models with significantly low values of RMSE and MBE. A strong correlation was also found between the observed and estimated sediment load values for the same model as the value of Nash-Sutcliffe model efficiency coefficient (R-square) was found to be quite high as well.

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References

  • Agalbjorn, S., Koncar, N., & Jones, A. J. (1997). A note on the gamma test. Neural Computing and Applications., 5, 131–133.

    Article  Google Scholar 

  • Agarwal, A., Mishra, S. K., Ran, S., & Singh, J. K. (2006). Simulation of runoff and sediment yield using artificial neural network. Biosystems Engineering, 94(4), 597–613.

    Article  Google Scholar 

  • Akrasi, S. A.., & Ansa-Asare, O. D. (2003) Assessing sediment and nutrient transport in the Pra basin of Ghana. West African Journal of Applied Ecology, 13.

  • Alam, U. Z. (1998). Water rationality: mediating the Indus waters treaty. University of Durham: Unpublished thesis.

    Google Scholar 

  • Ali, K. F., & De Boer, D. H. (2003). Construction of sediment budgets in large-scale drainage basins: the case of the upper Indus river. International Association of Hydrological Sciences Publication, 279, 206–215.

    Google Scholar 

  • Alizdeh, M. J., Joneyd, P. M., Motahhari, M., Ejlali, F., & Kiani, H. (2015). A wavelet ANFIS model to estimate sedimentation in dam reservoir. International Journal of Computer Applications, 114(9), 19–25.

    Article  Google Scholar 

  • Alp, M., & Cigizoglu, H. K. (2005). Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling & Software, 22(1), 2–13.

    Article  Google Scholar 

  • Azamathulla, H. M., Ab, G. A., Chang, C. K., Abu, H. Z., & Zakaria, N. A. (2010). Machine learning approach to predict sediment load—a case study. Clean-Soil Air Water, 38(10), 969–976.

    Article  CAS  Google Scholar 

  • Bhattacharya, B., Price, R. K., & Solomatine, D. P. (2007). Machine learning approach to modelling sediment transport. Journal of Hyraulic Engineering, 133(4), 440–450.

    Article  Google Scholar 

  • Cigizoglu, H. K. (2004). Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances in Water Resources, 27, 185–195.

    Article  Google Scholar 

  • Cobaner, M., Unal, B., & Kisi, O. (2009). Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydrometeorological data. Journal of Hydrology, 367, 52–61.

    Article  Google Scholar 

  • Environmental Water Resources Institute, U.W.R.R.C., Gross Solids Technical Committee. (2007). ASCE guideline for monitoring storm water gross solids.

  • Evans, D. (2002). Database searches for qualitative research. Journal of the Medical Library Association, 90(3), 290–293.

    Google Scholar 

  • Fine T. L. (1999) “Feedforward neural network methodology” (statistics for engineering and information science). 340 pp. Springer

  • Fletcher, R. (1987). Practical methods of optimization. New York, N.Y.: Wiley.

    Google Scholar 

  • Tayfur, G. (2002). Artificial neural networks for sheet sediment transport. Hydrological Sciences Journal, 47(6), 879–892.

    Article  Google Scholar 

  • Habib-ur-Rehman, M. (2001). Regional scale soil erosion and sediment transport modeling. Ph.D. Thesis, University of Tokyo, pp. 183.

  • Hassan, M., Shamim, M. A., Hashmi, H. N., Ashiq, S. Z., Ahmed, I., Pasha, G. A., Naeem, U. A., Ghumman, A. R., & Han, D. (2014). Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques. Earth Science Informatics, 8(2), 337–352.

    Article  Google Scholar 

  • Haykin, S. (1999). Neural networks: a comprehensive foundation (2nd ed., ). USA: Prentice-Hall.

    Google Scholar 

  • Hays, D., & Wu, P. (2001). “Simple approach to TSS source strength estimates” proceedings, 21st annual meeting of the western dredging association (WEDA XXI) and 33rd annual Texas A&M dredging seminar. Texas: Houston.

    Google Scholar 

  • Heng, S., & Suetsugi, T. (2013). Using artificial neural network to estimate sediment load in ungauged catchments of the tonle sap river basin, Cambodia. Journal of Water Resource and Protection, 5, 111–123.

    Article  Google Scholar 

  • Jain, A., Sudheer, K. P., & Srinivasulu, S. (2004). Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrological Processes, 18, 571–581.

    Article  Google Scholar 

  • Jason W. Osborne, (2010) Improving your data transformations; applying the box-Cox transformation Practical Assessment, Research & Evaluation 15(12)

  • Jie L.C., Yu S.T., (2011) “Suspended sediment load estimate using support vector machines in Kaoping River basin” 978–1–61284–459–6/11 2011 IEEE

  • Jones, A. J. (2004). New tools in non-linear modelling and prediction. Computational Management Science, 1, 109–149.

    Article  Google Scholar 

  • Kisi, Ö. (2004a). River flow modeling using artificial neural networks. Journal of Hydrologic Engineering, 9(1), 60–63.

  • Kisi, O. (2004b). Multi-layer perceptrons with levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrological Sciences Journal, 49(6), 1025–1040.

    Article  Google Scholar 

  • Kisi, O. (2005). Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal, 50(4), 683–696.

    Google Scholar 

  • Kisi, O., Yuksel, I., & Dogan, E. (2008). Modeling daily suspended sediment of rivers in Turkey using several data-driven techniques. Hydrological Sciences Journal, 53(6), 1270–1285.

    Article  Google Scholar 

  • Kux, D. (2006). India Pakistan negotiations: is past still prologue? In Perspective Series. Washington, DC: United States Institute of Peace.

    Google Scholar 

  • Lane, L. J., & Nicholas, M. H. (1997). A hydrologic method for sediment transport and yield. In S. Y. Wang, E. J. J. Langendeon, & F. D. Shields, Jr. (Eds.), Management of Landscapes Disturbed by Channel Incision (pp. 365–370). University of Mississippi, Oxford, MS: Center for Comp. Hydrosci. And Eng.

  • Lin, B., & Namin, M. M. (2005). Modelling suspended sediment transport using an integrated numerical and ANNs model. Journal of Hydraulic Research, 43(3), 302–310.

    Article  Google Scholar 

  • Lekkas, D. F., Imrie, C. E., & Lees, M. J. (2001). Improved non-linear transfer function and neural network methods of flow routing for real-time forecasting. Journal of Hydroinformatics, 3, 153–164.

    Google Scholar 

  • Maier, H. R., & Dandy, G. C. (1998). Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study. Environmental Modelling and Software, 13, 179–191.

    Article  Google Scholar 

  • Minsky, M., & Papert, S. (1969). Perceptrons. Cambridge, MA: MIT Press.

    Google Scholar 

  • McCulloch, W. S., & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7, 115–133. Reprinted in McCulloch 1964, pp. 16–39.

  • Mustafa, M. R., Isa, M. H., & Rezaur, R. B. (2011). Comparison of artificial neural network for prediction of suspended sediment discharge in river—a case study in Malaysia. World Academy of Science, Engineering and Technology, 81, 372–376.

    Google Scholar 

  • Nagy, H. M., Watanabe, K., & Hirano, M. (2002). Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering (ASCE), 128(6), 588–595.

    Article  Google Scholar 

  • Nakai O. (1978) “Turbidity generated by dredging projects, management of bottom sediments containing toxic substances” Proceedings of the third United States-Japan experts meeting, EPA-600/3–78–084, 1–47

  • Nourani, V. (2009). “Using artificial neural networks (ANNs) for sediment load forecasting of talkherood river mouth”. Journal of Urban and Environmental Engineering, 3(1), 1–6.

    Article  Google Scholar 

  • Nourani, V., Parhizkar, M., Vousoughi, F. D., & Amini, B. (2014). Capability of artificial neural network for detecting hysteresis phenomenon involved in hydrological processes. Journal of Hydrologic Engineering, 19, 896–906.

    Article  Google Scholar 

  • Olyaie, E., Banejad, H., Chau, K. W., & Melesse, A. M. (2015). “A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States”. Environmental Monitoring and Assessment, 187(4), 189.

    Article  Google Scholar 

  • Painter, D. J. (1981). Steeplands erosion processes and their assessment. In T. R. H. Davis, & A. J. Pearce (Eds.), Erosion and sediment stransport in Pacific Rim steeplands. IAHS_AISH publ. 132 (pp. 2–20). Washington, DC: International Association of Hydrological Sciences.

  • Partal, T., & Cigizoglu, H. K. (2008). Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. Journal of Hydrology, 358, 317–331.

    Article  Google Scholar 

  • Pennekamp, J. G. S., Epskamp, R. J. C., Rosenbrand, W. F., Mullie, A., Wessel, G. L., Arts, T., & Deibel, I. K. (1996). Turbidity caused by dredging; viewed in perspective. Terra et Aqua, 64, 10–17.

    Google Scholar 

  • Rajaee, T., Mirbagheri, S. A., Zounemat-kermani, M., & Nourani, V. (2009). Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. The Science of the Total Environment, 407, 4916–4927.

    Article  CAS  Google Scholar 

  • Remesan, R., Shamim, M. A., & Han, D. (2008). Model data selection using gamma test for daily solar radiation estimation. Hydrological Processes, 22, 4301–4309.

    Article  Google Scholar 

  • Remesan, R., Shamim, M. A., Han, D., & Mathew, J. (2009). Runoff prediction using an integrated hybrid modelling scheme. Journal of Hydrology, 372(1), 48–60.

    Article  Google Scholar 

  • Remesan, R., Shamim, M. A., Ahmadi, A., & Han, D. (2010). Effect of data time interval on real-time flood forecating. Journal of Hydroinformatics, 12(4), 396–407.

    Article  Google Scholar 

  • Roesner, L., Qian, Y., Criswell, M., Stromberger, M., & Klein, S. (2006). Long-term effects of landscape irrigation using household graywater– literature review and synthesis. Water Environment Research Foundation: Resource Document.

    Google Scholar 

  • Rosenblatt, F. (1962). Principles of neurodynamics; perceptrons and the theory of brain mechanisms. Spartan Books: Washington.

    Google Scholar 

  • Shamim, M. A., Hassan, M., Ahmad, S., & Zeeshan, M. (2015). A comparison of artificial neural networks (ANN) and local linear regression (LLR) techniques for predicting monthly reservoir levels. Journal of Korean Society for Civil Engineering(in Press). doi:10.1007/s12205-015-0298-z.

    Google Scholar 

  • Shamim, M. A., Bray, M., Remesan, R., & Han, D. (2014). A hybrid modelling approach for assessing solar radiation. Theoretical and Applied Climatology(in Press). doi:10.1007/s00704-014-1301-1.

    Google Scholar 

  • Shamim, M. A., Remesan, R., Han, D., & Ghumman, A. R. (2010). Solar radiation estimations in ungauged catchments. Proceedings of the ICE. Water Management, 163(7), 349–359.

    Google Scholar 

  • Shamim, M. A., Ghumman, A. R., & Ghani, U. (2004). Forecasting groundwater contamination using artificial neural networks. In International conference on water resources and arid environments, December 2004. Riyadh: Saudi Arabia: King Saud University.

    Google Scholar 

  • Shamseldin, A. Y. (2010). Artificial neural network model for river flow forecasting in a developing country. Journal of Hydroinformatics, 12, 22–35.

    Article  Google Scholar 

  • Stefánsson, A., Končar, N., & Jones, A. J. (1997). A note on the gamma test. Neural Computing Applications, 5, 131–133.

    Article  Google Scholar 

  • Tayfur, G., & Guldal, V. (2006). Artificial neural networks for estimating daily total suspended sediment in natural streams. Nordic Hydrology, 37, 69–79.

    Google Scholar 

  • Weaver, W., & Madej, M. A. (1981). Erosion control techniques used in Redwood National Park, Northern California 1978-1979. In Proceedings of a Symposium on Erosion and Sediment Transport in Pacific Rim Steeplands, Christchurch, New Zealand (pp. 640–645). Washington, DC: International Association of Hydrological Sciences Publication Number 132.

  • Zhu, Y. M., Lu, X. X., & Zhou, Y. (2007). “Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang River in the Upper Yangtze Catchment. China” Geomorphology, 84, 111–125.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Mirpur University of Science & Technology, Mirpur, A.K., Pakistan

    Muhammad Hassan, Imran Mehmood, Imtiaz Ahmed, Syed Zishan Ashiq & Anwar Khitab

  2. Department of Civil Engineering, Bursa Orhangazi Üniversitesi, Bursa, Turkey

    M. Ali Shamim

  3. Department of Civil Engineering, National University of Sciences & Technology, Islamabad, Pakistan

    Ali Sikandar

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  1. Muhammad Hassan
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  2. M. Ali Shamim
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  4. Imran Mehmood
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Correspondence to Muhammad Hassan.

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Hassan, M., Ali Shamim, M., Sikandar, A. et al. Development of sediment load estimation models by using artificial neural networking techniques. Environ Monit Assess 187, 686 (2015). https://doi.org/10.1007/s10661-015-4866-y

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