Multi Day Ahead Flood Predictionin South Asian Tropical Zone Using Deep Learning

Abstract A reliable and accurate flood forecasting procedure is a critical need due to the hazardous nature of the disaster. Researchers are increasingly favoring innovative approaches with enhanced accuracy, such as machine learning models, over traditional methods for this task. However, lack of such studies regarding South Asian tropical region, which has its own climate characteristics, was unidentified as a major issue. This research delves into the viability of employing ANN, LSTM, BLSTM, ConvLSTM2D and Transformer models for multi-day ahead flood simulation. One-day, two-days and three-days were selected as lead times for the task considering the lower reaches of the Mahaweli catchment in Sri Lanka, which is mostly affected by the Northeast Monsoon. The prediction capability of extreme stream flows was also of interest. Observed rainfall data from three nearby rain gauges, along with historical discharges of the target river gauge, serve as input features for the models. The ANN model showed the worst performance, having the mean NSE of 0.67. An improved performance was observed from the Transformer compared to the LSTM based models, especially in multiple day ahead forecasting scenarios. For all the models, the forecasting capability of extreme water levels drops down drastically when the prediction lead time is increased.