Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data

Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, an adaptive neuro-fuzzy approach is proposed to estimate suspended sediment concentration on rivers. The daily rainfall, streamflow and suspended sediment concentration data from Mad River Catchment near Arcata, USA are used as a case study. In the first part of the study, various combinations of current daily rainfall, streamflow and past daily streamflow, suspended sediment data are used as inputs to the neuro-fuzzy computing technique so as to estimate current suspended sediment. In the second part of the study, the potential of neuro-fuzzy technique is compared with those of the three different artificial neural networks (ANN) techniques, namely, the generalized regression neural networks (GRNN), radial basis neural networks (RBNN) and multi-layer perceptron (MLP) and two different sediment rating curves (SRC). The comparison results reveal that the neuro-fuzzy models perform better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study.