RUNOFF ANALYSIS INTO THE LOW-LYING LAKE BY USING MACHINE LEARNING

Abstract

 The model to reproduce the runoff into low-lying lake is developed by using deep learning method. The model was composed of 4-layer feed forward network and as training algorithm Adam was applied. The target area is Toyanogata lake which is located at Niigata prefecture. Input data were hourly precipitation data and hourly data of quantity of drainage water from Toyanogata lake, and output data was hourly runoff to the lake. Mainly the examination was conducted about input data; how long previous data of precipitation and quantity of drainage water should be input to improve accuracy of the model. And the input length which stabilized accuracy of the model was found in both of condition that precipitation data was only input and that precipitation data and drainage data were input. In addition, comparison was made about relationship between construction of the model and accuracy model between 3-layer ANN model and deep learning model.