COMPARISON OF MACHINE LEARNING METHODS FOR THE PREDICTION OF DAM WATER LEVEL DURING AN ABNORMAL FLOOD

Abstract

 In this paper, we report the result of comparing the water level prediction of the dam using several machine learning methods.Water level predictions using machine learning were calculated for Kanayama Dam and for Satsunaigawa Dam. The predictions were calculated from hydrological information for the basins of these two dams. The machine learning methods used for the predictions are the Random Forest, Fully Connected Neural Network (FCNN), Recurrent Neural Network methods, which has a structure for processing time series information, and the Elastic Net method, which is based on sparse modeling. The comparison found that FCNN and Elastic Net yield accurate results, with NS coefficients of 0.7 or greater. In Elastic Net, for cases other than those whose predicted rainfall has indeterminacy, the results were the most accurate, having NS coefficients of 0.7 or greater.