# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 1 Introduction
The relationship trained by a Supervised Learning algorithm is used either for predicting categorical values (i.e., classification tasks), or for predicting continuous values (i.e., regression tasks). Unsupervised Learning refers to learning of valuable information from training samples without labels, which is often used to identify patterns in big data or abstract low-dimensional representation of big data. Reinforcement Learning is a ML algorithm that carries out learning through interaction, in which agents use learning strategies to maximize rewards or achieve specific objectives in the process of interacting with the environment. With the boost of data abundance and computer power, ML has shown huge potential in climate, meteorological and oceanographic fields with satisfying results [26], and further applied to morphodynamics [27]. In terms of the application of ML, especially Neural Networks in Tropical Cyclone (TC), it covers the meteorological prediction of TC [23], the oceanographic prediction responding to the meteorological action [22], and the morpho-dynamical prediction responding to the oceanographic action [27]. The meteorological prediction involves the genesis forecast, the track forecast and the intensity forecast of TC. The genesis forecast of TC includes short-term forecast [28; 29; 30; 31; 32; 33; 34] which is defined as predicting whether TC precursors will evolve into TCs, and long-term forecast [35; 36; 37; 38; 39] which is defined as predicting the numbers of TCs in vulnerable areas for the upcoming TC season. The track forecast of TC investigated by many researchers focuses on the improvement of path prediction [40; 41], valuable predictor selection for building ML-based forecast models [42; 43; 44] and similarity search by which forecasters find similar historical TC cases to achieve the final forecast goal [45; 46; 47; 48; 49; 50; 51; 52; 53]. The intensity forecast of TC is usually concentrated on predicting the maximum wind speed or minimum sea level pressure at the center of a TC [54; 55; 56]. The oceanographic prediction responding to the meteorological action of TC using Neural Networks involves the prediction of storm surge [57; 58; 26; 59], extreme waves [60; 61; 62; 63; 64; 65], etc. The morpho-dynamical prediction responding to the oceanographic action of TC applies Neural Networks to sandbar movement [66; 67; 68], seasonal beach profile changes [69], and longshore sediment transport [70; 71]. In addition to TC prediction, Neural Networks has been widely applied in the prediction of tidal level [72; 73; 74; 75], wave height [61; 76] and coastal floods [77]. Compared with conventional method for tidal level prediction (harmonic analysis), the excellent nonlinear problem processing capability of Neural Networks solves the environmentally influenced noises of seasonal effects and TC-induced surge superposed on the astronomical tide level series [75]. Many studies have attempted single-layer Neural Networks or multi-layer Neural Networks (known as Deep Neural Networks, DNN) to predict tidal levels or storm surges [58; 78; 79; 80]. Since tidal levels and storm surges are varying with time, Recurrent Neural Networks (RNN), as a branch of Neural Networks, is a preferable option with better capability of predicting time series. Long Short-Term Memory (LSTM) proposed by [PERSON] and [PERSON] [81] is a state-of-art development of RNN, whose disadvantages, including the disappearance or the explosion of gradient when dealing with long sequence data, are overcome [82; 83]. As a favorable method for the description and prediction of time series, it is wide applied in multiple scientific and engineering fields, e.g., text recognition [84]. speech recognition [85], handwriting recognition [86], trajectory prediction [87], disease diagnosis [88], stock analysis [89], oil production [82], and electricity price [90]. LSTM is also employed to predict tidal level [73; 75].