A Study on Runoff Forecast by Aritifical Neural Network Model
XU Zhong min, LAN Yong chao, CHENG Guo dong (State Key Laboratory of Frozen Soil Engineering, CAREERI, CAS, Lanzhou Gansu 730000, China)
In spite of that nonlinear problem of hydrological runoff process have been noticed early, hydrologists elaborate various runoff forecast models by means of available experimental methods and dynamic model methods. Traditional models and methods depend on the parameters that have been initially well estimated. Traditional methods with well test interpretation are usually based on a combination of manual and automated techniques. There is obvious limitation in these methods, because the calibration of parameters involves artificial factors. In this paper a new method, named artificial neural network model(ANN), is presented and the Yinluo Gorge is taken as a case study. The runoff in the Yinluo Gorge is selected as an object to test the ANN method, and the back-propagation model, one of the typical artificial neural network models, is applied. How to forecast runoff by using the artificial neural network model and how to reach a fast convergence speed and high accuracy are described by choosing dynamic learning factor and inertia factor. It is revealed that the ANN method might be referred as an effective technique for runoff forecast. It is well known that the neural network model is tolerant to noise in the data. This property provides both advantage and disadvantage. The neural network is effective in recognizing noisy data, and then data smoothing is not necessary. However, the method sometimes recognizes similar something else that not actually belonging to the same pattern. Introducing the knowledge of well test interpretation into the forecast is useful in order to enhance the ability to express the relationship of cause and outcome.