Comparative Analysis of Training Methods and Different Data for the Rainfall-Runoff Predication Using Artificial Neural Networks

摘要

Hydrology and climatic monthly data's influence on training of Artificial Neural Networks (ANNs) for monthly rain fall prediction is investigated. For improved computed performances, efficiencies of the Conjugate Gradient (CG) and Levenberg-Marquardt (L-M) training algorithms are compared. The rain fall-run off influence is studied for a watershed in Northern Iran, representing a continuous rain fall-run off with stream flow regime occurring. The used data in ANN was hydrometric and climatic monthly data with 31 years duration from 1969 to 2000. For the mentioned model 27 year's data were used for its development but for the validation/testing of the model 4 years data was applied. Based on the results, the L-M algorithm is more efficient than the CG algorithm, so it is used to train six ANNs models for rain fall-runoff prediction at time step t+1 from time step t input. The used network in this study was MLP with B.P. (back propagation) algorithm. Model 1 uses enabled rain fall data as input dimension with use tree station, Model 2 uses enabled rain fall and average temperature, Model 3 uses enabled rain fall, average temperature and stream flow at time step t-1 and Model 4 uses enabled rain fall and stream flow at time step (t, t-1, t-2), Model 5 uses enabled rain fall and stream flow at time step (t, t-1, t-2.t-3) Model 6 uses enabled rain fall, average temperature and stream flow at time step (t-1, t-2). Validation stage Root Mean Square Error (RMSE), Root Mean Absolute Error (RMAE) and Correlation Coefficient (R) measures are: 0.07, 6×10~(-4), 0.99 (Model 1); 0.1, 9×10~(-4), 0.99 (Model 2); 0.01, 9×10~(-5), 1 (Model 3); 0.005, 6×10~(-5), 1 (Model 4); 0.001, 0.7×10~(-5), 1 (Model 5); 0.001, 6×10~(-5), 1 (Model 6) and, respectively. The influence of rain fall and stream flow at time step (t, t-1, t-2) on improved Model 4 performance is discussed.