An Artificial Neural Network Model for Estimating Fluvial Suspended Sediment Concentration Using MODIS Sensor Images (Case Study: Mollasani Hydrometric Station, Khouzestan Province)

The estimation of suspended sediment load is very important for water resources quantity and quality studies. The suspended sediment load is generally calculated by direct measurement of suspended sediment concentration (SSC) of a river or by using sediment rating curve (SRC) method. Direct measurement of the SSC is the most reliable but it is very expensive and time consuming. Also، the efficiency of the SRC method is low because it can substantially underpredict the high and overpredict the low loads. In this research، in order to consider the possibility of estimating the fluvial SSC، using reflectance of satellite images، the correlation between red and infrared bands of MODIS sensor and SSC of Karoun river at Molasani station for a period of 9 years (2003-2011) was considered. In this relation، two models (statistical simple linear regression and feed forward back propagation ANN) were used. The evaluation of models results showed that the efficiency of ANN model with having R2 =0. 89 and RMSE=122mg/l was better than the regression relation with R2 =0. 49 and RMSE=204mg/l. The research results showed that MODIS sensor images and ANN can be used together to estimate fluvial daily SSC in large rivers.