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Suspended sediment load prediction of river systems: An artificial neural network approach

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  • Melesse, A.M.
  • Ahmad, S.
  • McClain, M.E.
  • Wang, X.
  • Lim, Y.H.

Abstract

Information on suspended sediment load is crucial to water management and environmental protection. Suspended sediment loads for three major rivers (Mississippi, Missouri and Rio Grande) in USA are estimated using artificial neural network (ANN) modeling approach. A multilayer perceptron (MLP) ANN with an error back propagation algorithm, using historical daily and weekly hydroclimatological data (precipitation P(t), current discharge Q(t), antecedent discharge Q(t-1), and antecedent sediment load SL(t-1)), is used to predict the suspended sediment load SL(t) at the selected monitoring stations. Performance of ANN was evaluated using different combinations of input data sets, length of record for training, and temporal resolution (daily and weekly data). Results from ANN model were compared with results from multiple linear regressions (MLR), multiple non-linear regression (MNLR) and Autoregressive integrated moving average (ARIMA) using correlation coefficient (R), mean absolute percent error (MAPE) and model efficiency (E). Comparison of training period length was also made (4, 3 and 2 years of training and 1, 2 and 3 years of testing, respectively). The model efficiency (E) and R2 values were slightly higher for the 4 years of training and 1 year of testing (4Â *Â 1) for Mississippi River, indifferent for Missouri and slightly lower for Rio Grande River. Daily simulations using Input 1 (P(t), Q(t), Q(t-1), SL(t-1)) and three years of training and two years of testing (3Â *Â 2) performed better (R2 and E of 0.85 and 0.72, respectively) than the simulation with two years of training and three years of testing (2Â *Â 3) (R2 and E of 0.64 and 0.46, respectively). ANN predicted daily values using Input 1 and 3Â *Â 2 architecture for Missouri (R2Â =Â 0.97) and Mississippi (R2Â =Â 0.96) were better than those of Rio Grande (R2Â =Â 0.65). Daily predictions were better compared to weekly predictions for all three rivers due to higher correlation within daily than weekly data. ANN predictions for most simulations were superior compared to predictions using MLR, MNLR and ARIMA. The modeling approach presented in this paper can be potentially used to reduce the frequency of costly operations for sediment measurement where hydrological data is readily available.

Suggested Citation

  • Melesse, A.M. & Ahmad, S. & McClain, M.E. & Wang, X. & Lim, Y.H., 2011. "Suspended sediment load prediction of river systems: An artificial neural network approach," Agricultural Water Management, Elsevier, vol. 98(5), pages 855-866, March.
    • Handle: RePEc:eee:agiwat:v:98:y:2011:i:5:p:855-866
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    1. Sajjad Ahmad & Slobodan Simonovic, 2006. "An Intelligent Decision Support System for Management of Floods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(3), pages 391-410, June.
    2. S. Mosquera-Machado & Sajjad Ahmad, 2007. "Flood hazard assessment of Atrato River in Colombia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 591-609, March.
    3. Muhammad Ahmad & Abdul Ghumman & Sajjad Ahmad & Hashim Hashmi, 2010. "Estimation of a Unique Pair of Nash Model Parameters: An Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(12), pages 2971-2989, September.
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    16. Ozgur Kisi & Mohammad Zounemat-Kermani, 2016. "Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3979-3994, September.
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