Suspended Sediment Concentration Modeling Using Conventional and Machine Learning Approaches in the Thames River, London Ontario

Water resources management, hydraulic designs, environmental conservation, reservoir operation, river navigation and hydro-electric power generation all require reliable information and data about suspended sediment concentration (SSC). To predict such data, direct sampling and sediment rating curves (SRC) are commonly used. Direct sampling can be risky during extreme weather events and SRC may not provide satisfactory or dependable results, so engineers are developing new precise forecasting approaches. Various soft computing techniques have been used to model different hydrological and environmental problems, and have showed promising results. Prediction of SSC is a site-specific phenomenon and ought to be modeled for every river and creek. In this study, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models were compared with conventional SRC and linear regression methods. Using different combinations of observed SSC data and simultaneous stream discharge, water temperature, and electrical conductivity data for the Thames River at Byron Station, London Ontario from 1993 to 2016, several models were trained. Each model was evaluated using mean absolute error, root mean square error and the Nash-Sutcliffe efficiency coefficient. Results show that ANN models are more accurate than other modeling approaches for predicting SSC for this river.