Article

Ashvin Vaghani, S.S.A.S.I.T, Surat, Gujarat, India; Pradeepsinh Chauhan ,S.S.A.S.I.T, Surat, Gujarat,India; Margi Sheth ,S.S.A.S.I.T, Surat, Gujarat,India

Rainfall, runoff is highly non-linear and complicated phenomena in nature which requires modeling and simulation for the accurate prediction. The tool used to predict the rainfall-runoff pattern is by formulating the model using artificial neural network. The general application of artificial neural networks (ANNs) act as ‘black-box’ models of rainfall-runoff processes. The ANNs have been applied to both real and theoretical catchments with both measured and synthetically-generated rainfall-runoff data. The ANN tool has become an attractive alternative to the traditional statistical methods. This review considers the application of artificial neural networks (ANNs) to rainfall–runoff modeling. The effects of the number of layers are studied on the observed data and the result so obtained is compared with the observed values. Validation of the models is also discussed in the study. Different types of ANN Networks are compared with their architectures and based on their model performances. The present work involves Rainfall-Runoff modeling using Artificial Neural Network Using ANN software tool Neuro-solution, Linear Regression by least squares method using Microsoft office tool Excel is done also. The eight years’ data from 2001-2008 is utilized for analysis. The Rainfall-Runoff model is developed by applying Multilayer perceptron (MLP) and Linear Regression (LR) to predict daily Runoff as a function of daily rainfall for the catchment area under consideration of Damanganga basin. Neural network architecture is established for daily rainfall-runoff relationship for monsoon season data and yearly data. A linear regression model is also formulated. The seasonal data gives a better fit in comparison to yearly data and it gives higher value of coefficient of determination.