Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10871
Title: A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems : a case study in United States
Authors: Olyaie, E
Banejad, H
Chau, KW 
Melesse, AM
Keywords: ANFIS
ANN
Estimating
Soft computing
SRC
Suspended sediment load
WANN
Issue Date: 2015
Publisher: Springer
Source: Environmental monitoring and assessment, 2015, v. 187, no. 4 How to cite?
Journal: Environmental monitoring and assessment 
Abstract: Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.
URI: http://hdl.handle.net/10397/10871
ISSN: 0167-6369
EISSN: 1573-2959
DOI: 10.1007/s10661-015-4381-1
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