Simulation of suspended sediment based on gamma test,heuristic, and regression-based techniques

In the present study, four different heuristic techniques viz. multi-layer perceptron (MLP), radial basis function (RBF), self-organizing maps (SOM), and co-active neuro-fuzzy inference system (CANFIS) with hyperbolic tangent and sigmoid transfer functions and two regression-based techniques, i.e., multiple linear regression (MLR) and sediment-rating curve (SRC), were used for suspended sediment modeling. Gamma test (GT), correlation function (CF), M test, and trail–error procedure were applied for estimation of appropriate input variables as well as training data length. The results of the GT and CF suggested the five input variables (Q_t, Q_t?1, Q_t?2, S_t?1, and S_t?2, where Q_t?1 and S_t?1 indicate the discharge and sediment values of one previous day) as the best combination. The optimal training data length (75% of total data) was estimated by M test and trail–error procedure for development of the applied models. The MLP with sigmoid transfer function (M-2) performed better than the all other models. The results of sensitivity analysis indicated that the present-day discharge (Q_t), 1-day lag discharge (Q_t?1) and 1-day lag suspended sediment (S_t?1) are the most influenced parameters in modeling current day suspended sediment (S_t).