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2020 | OriginalPaper | Chapter

Prediction of Sedimentation in an Arid Watershed Using BPNN and ANFIS

Authors : Sandeep Samantaray, Abinash Sahoo, Dillip K. Ghose

Published in: ICT Analysis and Applications

Publisher: Springer Singapore

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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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Metadata
Title
Prediction of Sedimentation in an Arid Watershed Using BPNN and ANFIS
Authors
Sandeep Samantaray
Abinash Sahoo
Dillip K. Ghose
Copyright Year
2020
Publisher
Springer Singapore
Book
ICT Analysis and Applications

Print ISBN: 978-981-15-0629-1

Electronic ISBN: 978-981-15-0630-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0630-7_29