# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 1 Introduction  
Storm surge is a complex atmosphere-ocean coupled process which is characterized by the sudden occurrence of rising water and waves. It is widely known that the estimated maximum wind speed is a vital indicator of storm damage [1; 2; 3], because strong winds accompanied with low atmospheric pressure can push the water to pile up above normal levels [4]. Typhoons or hurricanes are the most energetic atmospheric force acting on coastal and estuarine waters and thus are the most serious natural disaster among marine disasters, which would cause significant changes in hydrodynamics like water level or storm surge [5; 6; 7; 8]. Due to the warmer sea surface water [9; 10; 11] induced by the trend of climate warming in recent years, typhoon average intensity is expected to increase by 14% over the Northwestern Pacific by 2100 [12]. Sea level rise is considered as an important factor of storm surge. The sea level rise projected in this century by many researchers [13; 14] will aggravate the threat from storm surge flooding, and the effects of sea level rise need to be considered to deal with the influence of climate changes on coastal areas. Storm size, which is usually represented by the radius of maximum windspeed, is another factor that is less focused on. [PERSON] et al. (2008) analyzed the observed historical storm data along with the idealized numerical simulation data to find that storm surge increases with storm size, especially for the case of intense storms on very shallow slopes [15]. China is one of the nations most prone to suffer from storm surge, as more than one-third of its coastal cities are located in the high-risk zones [16]. When storm surge coincides with high astronomical tides, exceptional high water levels can occur near the mouth of estuaries and rivers [17], which can cause dike overflowing, seawall failure andsalinity intrusion, resulting in a large economic loss [18] and a huge impact on water supply. Therefore, it is necessary to carry out research on observation and prediction of storm surge. Observations and numerical simulations have been the main ways to investigate the storm surge in the estuary area. A number of numerical simulation models have been developed to improve the forecasting of storm surge. [PERSON] et al. [19] coupled a statistical/deterministic hurricane model with the hydrodynamic model SLOSH to predict and assess the risks of New York area. They used this model to generate a large number of synthetic surge events and the model can be extended to consider the effect of future climate change. [PERSON] et al. [20] used FVCOM to simulate the direct landing typhoon and the offshore northward typhoon and found the responses of storm surge to the representative tracks and storm timing in the Yangtze River Estuary. [PERSON] et al. [21] developed a storm surge model established by ADCIRC and simulated the inundation caused by typhoon and applied an effective tool for predicting the risk of storm surge. The numerical simulation for the typhoon storm surge is time-consuming in most cases, while the emergency response to the disaster caused by storm surge requires a fast and reliable model for predictions of storm surge height so as to mitigate the environmental, social and economic damages. Machine Learning (ML) is a suitable surrogate method to ease the computational burden of physical process-based model [22]. ML refers to the method of learning general rules from limited observational data, and using these rules to make prediction or inferences [23]. Generally, ML algorithms are divided into three categories, i.e., Supervised Learning, Unsupervised Learning, and Reinforcement Learning [24; 25]. Supervised Learning (e.g., Bayesian Ridge Regression, Linear Regression, Elastic Net, Support Vector Machine, Gradient Boosted Decision Tree, XGBoost, and Neural Networks) aims to establish the relationship between the sample characteristics and labels, and each sample in the training set has a label.