Uncertainty Reduction of the Flood Stage Forecasting Using Neural Networks Model1

Abstract: The Elman Discrete Recurrent Neural Networks Model (EDRNNM), which is one of the special types of neural networks model, is developed and applied for the flood stage forecasting at the Musung station (No. 1) of the Wi‐stream catchment, which is one of the International Hydrological Program representative basins, Korea. A total of 135 different training patterns, which involve hidden nodes, standardization process, data length, and lead‐time, are selected for the minimization of the architectural uncertainty. The model parameters, such as optimal connection weights and biases, are estimated during the training performance of the EDRNNM, and we apply them to evaluate the validation performance of the EDRNNM. Sensitivity analysis is used to reduce the uncertainty of input data information of the EDRNNM. As the results of sensitivity analysis, the Improved EDRNNM consists of four input nodes resulting from the exclusion of Dongkok station (No.5) in initial five input nodes group of the EDRNNM. The accuracy of flood stage forecasting during the training and validation performances of the Improved EDRNNM remains the same as that of the EDRNNM. The Improved EDRNNM, therefore, gives highly reliable flood stage forecasting. The best optimal EDRNNM, so called the Improved EDRNNM, is determined by elimination of the uncertainties of architectural and input data information in this study. Consequently, we can avoid unnecessary data collection and operate the flood stage forecasting system economically.