A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States

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.

Differences in Stream Water Nitrate Concentrations between a Nitrogen-Saturated Upland Forest and a Downstream Mixed Land Use River Basin

“…Therefore, further investigation is required to analyze topographic dependence on denitrification and assimilation in urban areas. In addition, the loading and nitrogen reducing process is ambiguous in urban area and therefore modeling evaluation using fuzzy pattern-recognition may be useful [43][44][45][46].…”

Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

“…These interconnections are adjusted using an error convergence technique so that the network's response best matches the desired response. In general, the main advantage of the ANN technique over traditional methods is that it does not require information about the complex nature of the underlying process under consideration to be explicitly described in mathematical form WANG et al [2009], OLYAIE et al [2015. In the present study, the input and output data have been scaled to make them bounded in the intervals 0 and +1 using the standardization equation as follows:…”

“…The results showed that the performances of both particular RBF and MLP models were close to each other and capable to capture the exact pattern of the sediment data in the river. OLYAIE et al [2015] compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (AN-FIS), 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.…”

Estimation of photovoltaic potential for electricity self-sufficiency: A study case of military facilities in northwest Spain

“…This would require prior work on searching and obtaining multi-year data history for training the neural networks. Another option for dealing with uncertainty due to data scarcity would be the use of fuzzy sets [29][30][31] .…”