River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model

Accurate suspended sediment prediction is an integral component of sustainable water resources and environmental systems. This study considered artificial neural network (ANN), wavelet analysis and ANN combination (WANN), multilinear regression (MLR), and sediment rating curve (SRC) models for daily suspended sediment load (S) modeling in the Iowa River gauging station in the United States. In the proposed WANN model, discrete wavelet transform was linked to the ANN method. For this purpose, observed time series of river discharge (Q) and S were decomposed into several subtime series at different scales by discrete wavelet transform. Then these subtime series were imposed as inputs to the ANN method to predict one-day-ahead S. The results showed that the WANN model was in good agreement with the observed S values and that it performed better than the other models. The coefficient of efficiency was 0.81 for the WANN model and 0.67, 0.6, and 0.39 for the ANN, MLR, and SRC models, respectively. In addition, the WANN model presented relatively reasonable predictions for extreme S values, acceptably simulated the hysteresis phenomenon, and satisfactorily estimated the cumulative suspended sediment load. Wavelet transforms provide useful decompositions of primary time series, so that wavelet-transformed data improve the ability of a predicting model by capturing useful information on various resolution levels. The proposed WANN model can be considered as a relatively new application of a combined wavelet and ANN model for suspended sediment prediction. DOI:10.1061/(ASCE)HE.1943-5584.0000347. (C) 2011 American Society of Civil Engineers.