Neural Networks Model for Analysis of Input Information Uncertainty in the Small Catchment

Elman Discrete Recurrent Neural Networks Model(EDRNNM) was used to be a highly suitable flood stage forecasting tool yielding a very high degree of flood stage forecasting accuracy at Musung station(No.1) of Wi-stream, one of IHP representative basins in South Korea. A relative new approach, EDRNNM, has recurrent feedback nodes and virtual small memory in the structure. EDRNNM was trained by using two algorithms, namely, LMBP and RBP. The model parameters, optimal connection weights and biases, were estimated during training procedure. They were applied to evaluate model validation. Sensitivity analysis test was also performed to account for the uncertainty of input nodes information. The sensitivity analysis ap proach could suggest a reduction of one from five initially chosen input nodes. Because the uncertainty of input nodes information always result in uncertainty in model results, sensitivity analysis can help to reduce the uncertainty of EDRNNM application and management in the small catchment.

[1]  Rui Zou,et al.  Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty , 2002 .

[2]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[3]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[4]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[5]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[6]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[7]  Hyeon-Seok Sin,et al.  Spatial-Temporal Drought Analysis of South Korea Based On Neural Networks , 1999 .

[8]  Martin T. Hagan,et al.  Neural network design , 1995 .

[9]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[10]  Jong-Kwon Park,et al.  Hydrological Analysis using the Neural Networks in the Parallel Reservoir Groups, South Korea , 2003 .

[11]  Peggy A. Johnson,et al.  Modeling Uncertainty in Prediction of Pier Scour , 1996 .

[12]  Sungwon Kim,et al.  Forecasting of Flood Stage Using Neural Networks in the Nakdong River, South Korea , 2001 .

[13]  Shie-Yui Liong,et al.  River Stage Forecasting in Bangladesh: Neural Network Approach , 2000 .

[14]  Seong-Won Kim,et al.  Hydrological Forecasting Based on Hybrid Neural Networks in a Small Watershed , 2001 .

[15]  Bernard Bobée,et al.  Neural Network‐Based Long‐Term Hydropower Forecasting System , 2000 .