## 2 Literature Review  
[PERSON] and [PERSON] [13] applied a machine learning genetic programming (MLGP) model to obtain daily-suspended load. It was observed that the MLGP model outperformed the genetic expression programming (GEP) model. The prediction error, root-mean-square error (RMSE), obtained from the MLGP model was 175 ton/d, while for the GEP model, the RMSE was 231 ton/d. [PERSON] et al. [14] compared the genetic programming (GP) model with the support vector machine (SVM) and ANN models in daily-suspended load prediction. The results indicated that the GP model outperformed the SVM and ANN models. [PERSON] et al. [15] compared the SVM, ANN, and linear regression models to predict suspended sediment load. The results revealed that the SVM model outperformed the ANN and linear regression models. They applied 85% of the input data for training the soft computing models, and the remaining 15% of the data were applied for testing. Another paper studied the capabilities of SVM and ANN models to predict daily-suspended load. The suspended load was modeled by using the inputs of rainfall and streamflow. A gamma test was used to select input data [16]. It was found that the SVR model with radial basis kernel function presented more accurate outputs than other models. Another study investigated the performance of the ANN model in forecasting sediment movement by applying the assumptions of self-filtering sewer models [17]. It was found that the ANN model outperformed SVM and GP models. Velocity and shear stress were used as input data. Rainfall, discharge, and antecedent sediment load were used as input to the ANN model [18]. Outputs from the ANN model were benchmarked against multiple linear regression (MLR). The reported ANN model could be effectively applied to decrease the frequency of high-priced operations for sediment evaluations. [PERSON] and [PERSON] [19] applied a probabilistic system for forecasting daily-suspended loads. The accuracy of the probabilistic model was better than experimental rating curve methods. Statistical characteristics such as median, mean, and mode of the obtained probability distribution were the model outputs. [PERSON] and [PERSON] [20] applied a hybrid double-forward neural network (HDFNN) model to predict suspended load. The obtained peak value of suspended sediment load (SSL) showed that the HDFNN model was more suitable than the multilayer feed-forward neural network. A staking method was used to forecast SSL [21]. To investigate the suggested method, two case studies from the USA were considered. Streamflow and suspended sediment concentration were model inputs. The estimated outputs illustrated that the suggested method performed better than genetic programming and regression models. In a study in Turkey, the capabilities of SVM, ANN, and ANFIS models were studied to predict suspended load [22]. The simulation was made by applying two input variables, streamflow, and suspended sediment load. Results showed that the ANFIS model could give a lower RMSE than the other models. To estimate SSL, a multilayer perceptron (MLP) neural network model was trained by different algorithms [23]. The results indicated that the Levenberg-Marquardt algorithm could reach fast convergence. The performance of a scaled conjugate gradient (SCG) algorithm had the next priority. Hybrid tree models were used to forecast SSL [24]. Water temperature, water discharge, and electrical conductivity were model inputs. The Friedman test was applied to determine the best models. The results indicated that the hybrid tree models outperformed simple tree models. The random subspace error pruning (RSEP) model performed worse than other models. [PERSON] and [PERSON] [14] applied the evolutionary fuzzy (EF) method to forecast SSL. The prediction model was created by applying discharge data. The obtained results proved the high capability of the EF model for SSL prediction. They suggested that the model could be a useful tool for river engineering.