Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting

Abstract

Operational flood control systems depend on reliable and accurate forecasts with a suitable lead time to take necessary actions against flooding. This study proposed a Long Short-Term Memory based Encoder-Decoder (LSTM-ED) model for multi-step-ahead flood forecasting for the first time. The Shihmen Reservoir catchment in Taiwan constituted the case study. A total of 12,216 hourly hydrological data collected from 23 typhoon events were allocated into three datasets for model training, validation, and testing. The input sequence of the model contained hourly reservoir inflows and rainfall data (traced back to the previous 8 h) of ten gauge stations, and the output sequence stepped into 1- up to 6-hour-ahead reservoir inflow forecasts. A feed forward neural network-based Encoder-Decoder (FFNN-ED) model was established for comparison purposes. This study conducted model training a number of times with various initial weights to evaluate the accuracy, stability, and reliability of the constructed FFNN-ED and LSTM-ED models. The results demonstrated that both models, in general, could provide suitable multi-step ahead forecasts, and the proposed LSTM-ED model not only could effectively mimic the long-term dependence between rainfall and runoff sequences but also could make more reliable and accurate flood forecasts than the FFNN-ED model. Concerning the time delay between the time horizons of model inputs (rainfall) and model outputs (runoff), the impact assessment of this time-delay on model performance indicated that the LSTM-ED model achieved similar forecast performance when fed with antecedent rainfall either at a shorter horizon of 4 h in the past (T − 4) or at horizons longer than 7 h in the past (>T − 7). We conclude that the proposed LSTM-ED that translates and links the rainfall sequence with the runoff sequence can improve the reliability of flood forecasting and increase the interpretability of model internals.