Prediction of Suspended Sediment Using Hydrologic and Hydrogeomorphic Data within Intelligence Models

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ASADI H.; SHAHEDI K.; Sidle R.C.; Kalami Heris S.M.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2019 | Volume:15 | Issue:3 Page(s): 105-119

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Information Journal Paper

Title

Prediction of Suspended Sediment Using Hydrologic and Hydrogeomorphic Data within Intelligence Models

Author(s)

ASADI H. | SHAHEDI K. | Sidle R.C. | Kalami Heris S.M. | Issue Writer Certificate

Keywords

Suspended Sediment ModelingQ1

Artificial Neural NetworkQ2

Support Vector RegressionQ1

Index of Sediment ConnectivityQ1

Abstract

Accurate estimation of transported sediment by rivers plays an important role in water resources management. So the selection of proper methods for estimation of suspended sediment is an important objective to that goal. In this regard, application of intelligence models (e. g., ANN, SVR) has substantially improved the prediction of suspended sediment. Using these models, an important step in Suspended Sediment Modeling is the proper input selection since input vectors determine the structure of the model and, hence, influence model results. In the most studies, only climatic and hydrological variables have been used as suspended sediment estimators using data-driven models. Therefore, this study was designed to determine the effective and accessible geomorpholigical variables in suspended sediment estimation for the Tamar catchment. To accomplish this goal, the effect of the Index of Connectivity (IC) as a hydrogeomorphic input, in addition to the hydrologic inputs, was investigated. Comparison of the results for different input patterns indicated that using IC along with hydrological inputs improved the model efficiency reported by decrease in the root mean squared error (9. 63% and 26. 36%) and increase in the Nash– Sutcliffe efficiency (25. 80% and 21. 85%) and in the coefficient of determination (13. 20% and 45. 94%) respectively for ANN and SVR models.

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APA: Copy

ASADI, H., SHAHEDI, K., Sidle, R.C., & Kalami Heris, S.M.. (2019). Prediction of Suspended Sediment Using Hydrologic and Hydrogeomorphic Data within Intelligence Models. IRAN-WATER RESOURCES RESEARCH, 15(3 ), 105-119. SID. https://sid.ir/paper/100390/en

Vancouver: Copy

ASADI H., SHAHEDI K., Sidle R.C., Kalami Heris S.M.. Prediction of Suspended Sediment Using Hydrologic and Hydrogeomorphic Data within Intelligence Models. IRAN-WATER RESOURCES RESEARCH[Internet]. 2019;15(3 ):105-119. Available from: https://sid.ir/paper/100390/en

IEEE: Copy

H. ASADI, K. SHAHEDI, R.C. Sidle, and S.M. Kalami Heris, “Prediction of Suspended Sediment Using Hydrologic and Hydrogeomorphic Data within Intelligence Models,” IRAN-WATER RESOURCES RESEARCH, vol. 15, no. 3 , pp. 105–119, 2019, [Online]. Available: https://sid.ir/paper/100390/en

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