Abstract
Suspended sediment model and predicting its concentration in a natural stream are important fundamentals in managing water recourses policy worldwide. Present investigation considers adaptive neuro-fuzzy inference system (ANFIS) and backpropagation neural network (BPNN) to model suspended sediment load (SSL). Rainfall, temperature and SSL data are used to train and validate the model from Mahanadi river in Odisha, India. The estimation results obtained by using the neuro-fuzzy technique are tested and contrasted to those of artificial neural networks (ANNs). Root mean squared errors (RMSE) and coefficient of determination (R2) are utilized as assessing criterion to evaluate the model performances. Based on research finding ANFIS provides superlative value of R2 is 0.9625 and 0.9814, but for BPNN it delivers 0.9376 and 0.9592, respectively. Assessment outcomes show that ANFIS is better suited to apply for estimating suspended sediment daily.
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Samantaray, S., Sahoo, A., Ghose, D.K. (2020). Prediction of Sedimentation in an Arid Watershed Using BPNN and ANFIS. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-15-0630-7_29
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DOI: https://doi.org/10.1007/978-981-15-0630-7_29
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