A modelling approach for estimating suspended sediment concentrations for multiple rivers influenced by agriculture |
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| Authors: | Zacharie Sirabahenda André St-Hilaire Simon C Courtenay Ashley Alberto Michael R Van Den Heuvel |
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| Institution: | 1. INRS-ETE, Québec City, Quebec, Canada;2. Department of Biology, Canadian Rivers Institute, Fredericton, New Brunswick, Canada;3. Department of Biology, Canadian Rivers Institute, Fredericton, New Brunswick, Canada;4. School of Environment, Resources and Sustainability, University of Waterloo, Waterloo, ON, Canada;5. Canadian Water Network, Waterloo, ON, Canada;6. Department of Biology, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada |
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| Abstract: | A data-driven model based on an adaptive neuro-fuzzy inference system (ANFIS) was tested for the estimation of suspended sediment concentrations within watersheds influenced by agriculture. ANFIS models were developed using different combinations of inputs such as precipitation, streamflow, surface runoff and the watershed vulnerability index. A multi-watershed ANFIS model was also developed combining the datasets from all studied watersheds. The best results were obtained from a combination of precipitation, streamflow and watershed vulnerability index as input variables. Nash-Sutcliffe coefficients were improved for the multi-watershed ANFIS compared to watershed-specific ANFIS models. The introduction of the erosion vulnerability index significantly improved the ability of the ANFIS model to estimate suspended sediment concentrations within the watersheds. Furthermore, the inclusion of this index opens the possibility of using the ANFIS model to investigate the impact of land-use changes on sediment delivery. |
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| Keywords: | agriculture soil erodibility turbidity suspended sediment adaptive neuro-fuzzy inference system (ANFIS) |
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