Search

This paper considers storm surge prediction using a neural network and considering multiple physical characteristics. Based on the factors that influence storm surges and historical observation data, we divide the input to the neural network into time features extracted from the prediction target and the auxiliary features that affect storm surges, and construct a feature gate within multiple recurrent neural network (RNN) cells. Historical hurricane data are used to assess the effectiveness and accuracy of the proposed model. Comparative analysis against a long short-term memory (LSTM) storm surge prediction model is conducted to verify the prediction performance of the proposed method. The comparison results show that the multi-RNN model is superior to the LSTM model in terms of four evaluation metrics and for all lead times. In particular, the multi-RNN model accurately predicts the maximum storm surge water level, and the prediction results are more consistent with the rise and fall of the water. A comparison of the storm surge forecasts using inputs from different time intervals under different evaluation indices confirms the generalization and stability of our proposed model. The experiments of storm surge prediction at six stations further confirm the wide applicability of the model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]