For safety and survival at sea and on the shore, wave predictions are essential for marine-related activities, such as harbor operations, naval navigation, and other coastal and offshore activities. In general, wave height predictions rely heavily on numerical simulations. The computational cost of such a simulation can be very high (and it can be time-consuming), especially when considering a complex coastal area, since these simulations require high-resolution grids. This study utilized a deep learning technique called bidirectional long short-term memory (BiLSTM) for wave forecasting to save computing time and to produce accurate predictions. The deep learning method was trained using wave data obtained by a continuous numerical wave simulation using the SWAN wave model over a 20-year period with ECMWF ERA-5 wind data. We utilized highly spatially correlated wind as input for the deep learning method to select the best feature for wave forecasting. We chose an area with a complex geometry as the study case, an area in Indonesia’s Java Sea. We also compared the results of wave prediction using BiLSTM with those of other methods, i.e., LSTM, support vector regression (SVR), and a generalized regression neural network (GRNN). The forecasting results using the BiLSTM were the best, with a correlation coefficient of 0.96 and an RMSE value of 0.06.

This paper generalizes previous research on embedded wave generation in Boussinesq-type of equations for multi-directional surface water waves; the generation takes place by adding a suitable source term to the equations. Accurate generation is important to prevent influx errors in simulated waves downstream. For numerical implementations it may be a useful alternative to boundary influx methods since it is relatively easy to implement and will account accurately for the dispersive properties of the model. The source functions are unique only when the spatial and temporal constituents satisfy the dispersion relation of the model; this ambiguity can be used to choose the spatial extent over which the generation is applied by adjusting the given input signal. Elevation and velocity type of generation can then produce waves running forward or partly forward and partly backward as desired. The sources, derived for linear models, can also generate high waves in nonlinear equations provided an adjustment zone in which the nonlinearity grows gradually is used. Results of simulations are shown for various cases, including a focusing wave and oblique wave interaction

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively.

Wave prediction in a coastal area, especially with complex geometry, requires a numerical simulation with a high-resolution grid to capture wave propagation accurately. The resolution of the grid from global wave forecasting systems is usually too coarse to capture wave propagation in the coastal area. This problem is usually resolved by performing dynamic downscaling that simulates the global wave condition into a smaller domain with a high-resolution grid, which requires a high computational cost. This paper proposes a deep learning-based downscaling method for predicting a significant wave height in the coastal area from global wave forecasting data. We obtain high-resolution wave data by performing a continuous wave simulation using the SWAN model via nested simulations. The dataset is then used as the training data for the deep learning model. Here, we use the Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) as the deep learning models. We choose two study areas, an open sea with a swell-dominated area and a rather close sea with a wind-wave-dominated area. We validate the results of the downscaling with a wave observation, which shows good results.