Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad.

Suspended sediment is one of the most influential parameters on the water bodies' pollution. It can carry different pollutants with different concentration through the suspension movement in the flow. Therefore, it is of utmost importance to monitoring or modelling these loads so that an accurate sediment reduction strategy can be adopted. However, the monitoring process is laborious and time-consuming task. Thus, modelling is suggested as an alternative method. In this study, three different methods of artificial intelligence (i.e., random forest, support vector machine (Radial Basis Function), and artificial neural network) were employed to model and predict the suspended load at Sarai Station in Baghdad. To this end, observed flow rate (m3/s) and the corresponding suspended sediment concentration (mg/l) measured over the periods 1962-1981 and 2000-2010 were collected. Auto and partial correlation was used to identify the best combinations of input model data. The data was randomly partitioned into 75% for training and 25% for validation. The confidence interval was hypothesized to assess the uncertainty in the observed and predicted data. Whereas, the k-fold cross validation was used to quantify the uncertainty in the modelling results. The predictive modelling results for the three evaluated methods were assessed based on R2, RMSE, and NSE coefficient. Results show that random forest has the superior performance among the others. The total suspended sediment transported was estimated to be 72,734,852 ton during the period 2000-2010.