Abstract
Forecasting of river flow is an important process which provides primary and basic information about problems regarding design and operations of river System. A progressive utilization of extended records of rainfall and other climatic data is possible because of its availability. Artificial Neural Network (ANN) methods have been applied comprehensively for past few decades in forecasting of streamflow, and it has been proven that ANN techniques are far better than other forecasting methods. An appropriate length selection of training datasets is complicated, and there is no certainty in predictions of trained ANNs with new sets of data which makes this method more complicated. There are three different methods of ANN namely, Layered Recurrent Neural Network (LRNN), Coactive Neuro-Fuzzy Inference System (CANFIS) and Cascade Forward Back Propagation Neural Network (CFBPNN) are used for streamflow forecasting and results are evaluated. Based on the results, CANFIS was found to be better than other ANN techniques in monthly flow forecasting.
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Sridharam, S., Sahoo, A., Samantaray, S., Ghose, D.K. (2021). Assessment of Flow Discharge in a River Basin Through CFBPNN, LRNN and CANFIS. In: Satapathy, S.C., Bhateja, V., Ramakrishna Murty, M., Gia Nhu, N., Jayasri Kotti (eds) Communication Software and Networks. Lecture Notes in Networks and Systems, vol 134. Springer, Singapore. https://doi.org/10.1007/978-981-15-5397-4_78
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DOI: https://doi.org/10.1007/978-981-15-5397-4_78
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