Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

• Published in: International journal of environmental science and technology (Tehran) Vol. 7; no. 1; pp. 93 - 110
• Main Authors: Rajaee, T, Mirbagheri, S. A, Nourani, V, Alikhani, A
• Format: Journal Article
• Language: English
• Published: Iran Center for Environment and Energy Research and Studies (CEERS) 01.12.2010; Springer-Verlag
• Subjects: Aquatic Pollution; Artificial intelligence; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Science and Engineering; Hysteresis; Modeling; Sediment rating curve; Soil Science & Conservation; Waste Water Technology; Water Management; Water Pollution Control; Wavelet decomposition; Artificial intelligence; Modeling; Sediment rating curve; Hysteresis; Wavelet decomposition
• ISSN: 1735-1472; 1735-2630
• DOI: 10.1007/BF03326121

This study investigated the prediction of suspended sediment load in a gauging station in the USA by neuro-fuzzy, conjunction of wavelet analysis and neuro-fuzzy as well as conventional sediment rating curve models. In the proposed wavelet analysis and neuro-fuzzy model, observed time series of river discharge and suspended sediment load were decomposed at different scales by wavelet analysis. Then, total effective time series of discharge and suspended sediment load were imposed as inputs to the neuro-fuzzy model for prediction of suspended sediment load in one day ahead. Results showed that the wavelet analysis and neuro-fuzzy model performance was better in prediction rather than the neuro-fuzzy and sediment rating curve models. The wavelet analysis and neuro-fuzzy model produced reasonable predictions for the extreme values. Furthermore, the cumulative suspended sediment load estimated by this technique was closer to the actual data than the others one. Also, the model could be employed to simulate hysteresis phenomenon, while sediment rating curve method is incapable in this event.