Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.     

Wavelet and Neuro‐fuzzy Conjunction Approach for Suspended Sediment Prediction

Simulation approaches employed in suspended sediment processes are important in the areas of water resources and environmental engineering. In the current study, neuro‐fuzzy (NF), a combination of wavelet transform and neuro‐fuzzy (WNF), multi linear regression (MLR), and the conventional sediment rating curve (SRC) models were considered for suspended sediment load (S) modeling in a gauging station in the USA. In the proposed WNF model, the discrete wavelet analysis was linked to a NF approach. To achieve this aim, the observed time series of river flow discharge (Q) and S were decomposed to sub time series at different scales by discrete wavelet transform. Afterwards, the effective sub time series were added together to obtain a useful Q and S time series for prediction. Eventually, the obtained total time series were imposed as inputs to the NF method for daily S prediction. The results illustrated that the predicted values by the proposed WNF model were in good agreement with the observed S values and gave better results than other models. Furthermore, the WNF model satisfactorily estimated the cumulative suspended sediment load and produced relatively reasonable predictions for extreme values of S, while NF, MLR, and SRC models provided unacceptable predictions.     

Selenium transport and transformation modelling in soil columns and ground water contamination prediction

Selenium transport and transformation were simulated in a soil column. A one‐dimensional dynamic mathematical and computer model is formulated to simulate, selenate, selenite, selenomethionine, organic selenium, and gaseous selenium. This computer model is based on the mass balance equation, including convective transport, dispersive transport, surface adsorption, oxidation and reduction, volatilization, chemical and biological transformation. The mathematical solution is obtained by the finite difference implicit method. The model was verified by comparison of model results with experimental measurements and also using mass balance calculations in each time step of calculation. For example after 4 days of simulation, the simulated value of adsorbed selenate for depth of 20 cm is 0·2 µmol kg^?1 and the measured value is 0·25 µmol kg^?1. Therefore simulated results are in good agreement with measured values. With this study and its results the distribution of various forms of selenium in soil column to ground water table can be predicted. Copyright © 2008 John Wiley & Sons, Ltd.     

Improved Water Quality Prediction with Hybrid Wavelet-Genetic Programming Model and Shannon Entropy

Natural Resources Research - Prediction of biochemical oxygen demand (BOD) as the main pollution indicators of organic pollution in freshwater resources is necessary. In the present work, a hybrid...     

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

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.