Prediction of Flood Level Using LSTM and Watershed Hydrological Data

Accurate flood level prediction is crucial for mitigating flood damage caused by typhoons or localized heavy rainfall. However, predicting flood levels is challenging due to changes in river environments and external factors, such as dam or weir operations. To address these challenges, this study proposes a methodology for constructing an optimal combination of input data using basic hydrological information and predicting flood levels in real time through a deep learning model. The study focuses on identifying the best input data combination tailored to each river basin's characteristics, considering both natural runoff rivers and those influenced by dam discharges. The Long Short-Term Memory (LSTM) model, known for its superior performance in time-series forecasting, was employed. The results demonstrate high accuracy in flood level prediction, particularly within a 3-h lead time.