Accurate storm surge forecasting using the encoder-decoder long short term memory recurrent neural network

Abstract

The encoder-decoder LSTM (long short term memory) recurrent neural network is proposed to predict storm surge in Florida. Two types of hurricanes with six events are collected for training and testing. The previously observed meteorological data including the storm surge, the wind speed, the wind gust, the barometric pressure, and the air temperature are chosen as the inputs of model, while the future storm surge is designated as the output. The predicted results for 1, 3, 6, 9, and 12 h-lead time are obtained. Four indices are introduced to evaluate the accuracy and stability of the proposed model. Through comparing the predicted results by both models with the observed data, it is found that the encoder-decoder LSTM approach is more accurate than the convolutional neural network model for all considered cases. Furthermore, we notice that the combinations of storm surge, wind speed, wind gust, barometric pressure, and air temperature, and storm surge and wind speed give the best prediction for the first and the second types of hurricanes, respectively. Our work suggests that the encoder-decoder LSTM model has great potential in storm surge prediction. It is expected that the accuracy of this model can be further improved by introducing more observed data and considering more physical factors.