REAL TIME RIVER-STAGE PREDICTION BY ANN WITH OBSERVED RAINFALL AND RIVER-STAGE INFORMATION

Abstract

 This research is a development of our previous work, which was also based on artificial neural network usage for prediction of water levels at Hirakata station. This time we included rainfall information along with upstream water level data. The developed model uses a single hidden-layered feed-forward neural network. Prediction accuracy increased significantly compared to the previous research using a similar model. Regarding the whole validation period of three years, even though the model followed a certain pattern of decreasing performance with increasing lead time and increasing length of input data, the results still outperformed predictions obtained without using rainfall information. Some particular periods with peak events from the validation period were also checked. While all the results exhibited better outcomes compared to the previous model, this model did not follow a specific pattern corresponding with input data selection in any of these check periods.