하천 홍수 예측을 위한 CNN 기반의 수위 예측 모델 구현.

Flood damage can cause floods or tsunamis, which can result in enormous loss of life and property. In this regard, damage can be reduced by making a quick evacuation decision through flood prediction, and many studies are underway in this field to predict floods using time series data. In this paper, we propose a CNN-based time series prediction model. A CNN-based water level prediction model was implemented using the river level and precipitation, and the performance was confirmed by comparing it with the LSTM and GRU models, which are often used for time series prediction. In addition, by checking the performance difference according to the size of the input data, it was possible to find the points to be supplemented, and it was confirmed that better performance than LSTM and GRU could be obtained. Through this, it is thought that it can be utilized as an initial study for flood prediction. [ABSTRACT FROM AUTHOR]