Prediction of Suspended Sediment Load Using Radial Basis Neural Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Prediction of suspended sediment is vital for soil erosion during peak periods of inundation. The present work focused on development of modeling for suspended sediment concentration via Radial Basis Neural Network (RBNN) models. Parameters such as rainfall, discharge have been collected on daily basis from Rajghat gauging station of Subarnarekha basin to develop the model. Different architectures of models are fixed to envisage the performance of models during July, August, and September of monsoon period for measuring suspended sediment load. The individual best performances for different models are found out to measure sediment load during peak period of monsoon. Among July, August, and September, the model performance says the highest potential of erosion occurs during the month of September from the nearest zone of watersheds to the river basin during the peak period of flood. This work brings an idea for indirect measurement of sediment delivery ratio.

Keywords

Rainfall Runoff Suspended sediment Radial basis neural networks 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Institute of TechnologySilcharIndia

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