Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran)

Document Type: Research Paper

Authors

Lorestan University, Khorramabad, Iran

Abstract

Sediment transport constantly influences river and civil structures and the lack of
information about its exact amount makes management efforts less effective. Hence,
achieving a proper procedure to estimate the sediment load in rivers is important. We used
support vector machine model to estimate the sediments of the Kakareza River in Lorestan
Province and the results were compared with those obtained by gene expression
programming. The parameter of flow discharge for input in different time lags and the
parameter of sediment for output during 1992-2012 were considered. Criteria including
correlation coefficient, root mean square error and mean absolute error were used to
evaluate and also compare the performance of models. With regards to accuracy, the
support vector machine model showed the highest correlation coefficient (0.994), minimum
root mean square error (0.001 ton/day) and the mean absolute error (0.001 ton/day) which
was initiated at verification stage. The results also showed that the support vector machine
has great capability to estimate the minimum and maximum values for sediment discharge.

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