River Water Level Prediction Modelling using Artificial Neural Network and Multiple Linear Regression
Nowadays, Prediction modelling has become one of the most popular research areas among researchers/scientists around the world. In this study, the size of the training data is about 60%, validation data and testing set is about 20% of the total available data. In this paper, we have developed and tested feed-forward neural network architectures optimized with Levenberg-Marquardt back-propagation with transig activation function in hidden and output layers in predicting monthly river water elevation. Also, in this approach, the multiple linear regression equation to estimate monthly river water level was generated by using precipitation, discharge and return period as predictor variables. In this project, the results show the coefficient of determination (R2) between the predicted and actual output using both Artificial Neural Network and Multiple Linear Regression model for the monthly peak, monthly average and monthly minimum of Brahmaputra, Pagladia and Puthimari River.
 Abebe A.J. & Price R.K. (2004). Information theory and neural networks for managing uncertainty in flood routing. Journal of Computing in Civil Engineering ASCE, 18(4), 373-380.
 Badejo, Temitope O., Uduodo, & Daniel. (2014). Modelling and prediction of water level for a coastal zone using artificial neural networks. International Journal of Computational Engineering Research, 4(6), 26-41.
 The MathWorks Inc. (2009). Neural network toolbox for use with MATLAB. Available at: https://www.mathworks.com/access/helpdesk/help/toolbox/neuralnetwork/.
 Water Resource. (2019). Government of Assam. Available at: https://awrmis.assam.gov.in/.
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