Measuring Discharge Using Back-Propagation Neural Network: A Case Study on Brahmani River Basin

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

Abstract

Prediction of discharge (runoff) is vital for flood control during peak periods of flow. The present work is focused on the prediction discharge using back-propagation neural network (BPNN) models. Parameters like stage (water level) have been collected on daily basis from Govindpur basins on River Brahmani to estimate discharge using BPNN model. Different architectures of models are trained and tested to predict the performance of models during June, July, and August of monsoon period for measuring discharges at the proposed station. The individual best performances for different models are found out to measure discharges during peak period of monsoon. Among June, July, and August, the model performance says the highest flow occurs during the month of July for the study period.

Keywords

Stage Discharge Neural networks Back-propagation 

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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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