Precipitation-runoff Simulation with Neural Network(Case study: Nasa Bam Plain)
Subject Areas : محاسبات نرم در علوم مهندسی
mehdi shahrokhi sardoo
1
(
Islamic Azad University Jiroft branch
)
mojtaba jafari kermanipour
2
(
Faculty of Engineering, Shahid Bahonar University, Kerman
)
Keywords: rainfall-runoff simulation, artificial neural network, Nasa Bam watershed,
Abstract :
Short-term runoff forecasting is of particular importance due to its direct relationship with how managers interact with life risks caused by floods. In this research, by using artificial neural networks, simulation of rainfall-runoff process has been done on a daily basis in the Nasa Bam watershed. In order to predict the future process of using the water resources of the mentioned plain, different combinations of rainfall and temperature data and discharge and discharge difference of two consecutive days were used. The number of hidden layer neurons in the neural network varied between 2 and 10 neurons. The statistical criteria of root mean square error RMSE, mean absolute value of error MAE and correlation coefficient R were used to evaluate and compare the performance of neural networks in runoff forecasting. The results showed that by having 2 inputs and feedforward neural network or 1 input and newrbe network, the best performance was achieved and the rainfall-runoff process was predicted with higher accuracy.
