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Title

SUSPENDED SEDIMENT PREDICTION USING TIME SERIES AND ARTIFICIAL NEURAL NETWORKS MODELS (CASE STUDY: GHAZAGHLY STATION IN GORGANROUD RIVER)

Pages

 Start Page 216 | End Page 225

Abstract

 Accurate estimation of SUSPENDED SEDIMENT in rivers is very important from different aspects including agriculture, soil conservation, shipping, dam construction and aquatic research. There are different methods for SUSPENDED SEDIMENT estimation. In the present study to evaluate the ability of time-series models including MARKOV and ARIMA in predicting SUSPENDED SEDIMENT and to compare their results to Artificial Neural Networks it was tried to use daily suspended data from GHAZAGHLY station of Gorganroud River, as average monthly values in Minitab 16 software and Neurosolutions 5, and finally SUSPENDED SEDIMENT was predicted for 111 months. Calculation of the error measurement indices including RMSE and NMSE based on the results of this study showed a good ability of Artificial Neural Network models in estimating average monthly SUSPENDED SEDIMENT. On the other hand between time series models, MARKOV model has better ability in estimating monthly SUSPENDED SEDIMENT in comparison to the ARIMA model.

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