Long Short-Term Memory Recurrent Neural Network for Tidal Level Forecasting

Abstract

Tide is a phenomenon of water level change caused by gravity. Tidal level forecasting is not only a key theoretical topic but also crucial in coastal and ocean engineering applications. The waiting time before a cargo ship enters a port affects the efficiency of cargo transportation, the tidal difference affects the establishment of turbine generators, and an excessive tidal water level reduces vessel safety. With the proliferation of information technology, the application of deep learning models in the analysis and study of hydrological problems has become increasingly common. This study proposed a deep learning model to predict the tidal water level. A forecasting model was developed on the basis of the long short-term memory (LSTM) recurrent neural network for predicting the water levels of 17 harbors in Taiwan. Tidal water level data for 21 years were collected from different observation stations. To objectively evaluate model performance, the developed model was compared with six other forecasting models in terms of the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the forecasting results. The results indicated that the LSTM model had the lowest forecasting error for the tidal water level for up to 30 days. The average MAPE and RMSE values for the developed model were 6.97% and 0.049 m, respectively; thus, the model could effectively reduce the overlapping problems caused by machine learning methods in continuous forecasting. INDEX TERMS-Deep learning, long short-term memory, tidal level forecasting, time series.

AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

“…Consequently, the accuracy of vessel trajectory prediction could be increased. An LSTM network is a special RNN that is suitable for analyzing sequential data [57], [58]. In an LSTM network, a memory cell replaces the hidden layer function that is present in a traditional RNN.…”

Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan

“…Prevention is more effective than a cure. Lastly, the LSTM model can also be widely applied in many fields, such as vessel trajectory prediction [59], tidal level forecasting [60], financial market forecasting [61], and real-time crash risk prediction.…”

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

“…The morphodynamical 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].…”

“…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].…”

“…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]. However, when it comes to dealing with the sea level during the typhoon period, which is composed of the pure tidal level and the intense typhoon-induced nonlinearity, it becomes complicated for prediction.…”