RIVER WATER LEVEL PREDICTION USING STACKING

Abstract

The purpose of this study is to improve the accuracy of water level prediction several hours ahead in order to carry out more appropriate flood control activities and evacuation behaviors against flood disaster.

The target of prediction was the Hiwatashi basin of the Oyodo River system, and the water level prediction was carried out by using the information of the upstream water level observatories and the rainfall observatories around the basin. As the water level prediction model, GBDT (Gradient Boosting Decision Tree) model was used to predict for 1 to 6 hours. Prediction of stacking model is performed stepwise as follows; First, a short time prediction is carried out. Then the prediction result is added as a feature quantity for the next time prediction. By repeating the same procedure, we made prediction model up to 6 hours ahead. We compared the accuracy of the constructed model with the model without stacking, and confirmed the accuracy improvement by the stacking model.