Storm Surge Forecast Using an Encoder–Decoder Recurrent Neural Network Model

• Published in: Journal of Marine Science and Engineering
• Main Authors: Zhangping Wei, Hai Cong Nguyen
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: storm surge; Atlantic hurricanes; multivariate forecast; recurrent neural network; encoder–decoder model; long short-term memory model; Naval architecture. Shipbuilding. Marine engineering; VM1-989; Oceanography; GC1-1581; North Atlantic
• Online Access: https://doi.org/10.3390/jmse10121980; https://doaj.org/article/9dbee1e229634ff39102c863a76aa16c

This study presents an encoder–decoder neural network model to forecast storm surges on the US North Atlantic Coast. The proposed multivariate time-series forecast model consists of two long short-term memory (LSTM) models. The first LSTM model encodes the input sequence, including storm position, central pressure, and the radius of the maximum winds to an internal state. The second LSTM model decodes the internal state to forecast the storm surge water level and velocity. The neural network model was developed based on a storm surge dataset generated by the North Atlantic Comprehensive Coastal Study using a physics-based storm surge model. The neural network model was trained to predict storm surges at three forecast lead times ranging from 3 h to 12 h by learning the correlation between the past storm conditions and future storm hazards. The results show that the computationally efficient neural network model can forecast a storm in a fraction of one second. The neural network model not only forecasts peak surges, but also predicts the time-series profile of a storm. Furthermore, the model is highly versatile, and it can forecast storm surges generated by different sizes and strengths of bypassing and landfalling storms. Overall, this work demonstrates the success of data-driven approaches to improve coastal hazard research.