Rainfall Runoff Modeling by Artificial Neural Network - A Case Study of Chotki Bharghi Watershed in Damodar Barakar Basin, Jharkhand

Research areas:

• Volume 4,Issue 2, Sept. 2015

Year:

2015

Type of Publication:

Article

Keywords:

Chotki-Bharghi, MLP Model, MATLAB, MSE, Sediment Yield, Simulation

Authors:

Srinidhi Jha; Ajai Singh

Journal:

IJAIM

Volume:

4

Number:

2

Pages:

69-73

Month:

September

Abstract:

Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the simulation of sediment yield at the outlet of Chotki-Bharghi watershed by using Multilayer Perceptron model. The input data of rainfall and runoff of 2004-2008 were used for model training and validation. The whole computation was performed by using MATLAB capability of ANN. In this paper, trial and error method has been applied and from the total number of 194 input data set, 65% have been used as training data set, while 15% have been used as testing data set and 20% have been used as validation dataset. Three scenarios of input set of data were employed and it was observed that only input set with 3 hidden layer node and 2 days lagged input data performed best with Lavenberg Marquardt training algorithm in the estimation of sediment yield. The MLP model estimates the sediment yield values more accurately and with less uncertainty. It could be stated that MLP model based on simple input could be used for estimation of monthly sediment yield, missing data, and testing the accuracy and performance of other models.

Full text: IJAIM_479_Final.pdf