What machine learning algorithm is best suited for predicting short-term tidal current velocities using historical data and real-time sensor inputs?

A Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, is generally best suited for predicting short-term tidal current velocities using historical data and real-time sensor inputs due to its ability to effectively capture temporal dependencies and long-range patterns in sequential data. Tidal current velocities exhibit strong temporal dependencies, meaning that the current velocity at a given time is highly correlated with the current velocities at previous times. RNNs are a class of neural networks designed to process sequential data by maintaining a hidden state that captures information about past inputs. This allows them to learn and predict patterns that depend on the sequence of data. However, standard RNNs can struggle with long-range dependencies, meaning they may not be able to effectively capture patterns that depend on data points that are far apart in time. LSTMs are a type of RNN that are specifically designed to address this issue. LSTMs have a more complex architecture than standard RNNs, including memory cells and gates that allow them to selectively store and retrieve information over long periods. This makes them well-suited for modeling time series data with long-range dependencies, such as tidal current velocities. By training an LSTM network on historical tidal current data and incorporating real-time sensor inputs, such as current meter readings and water level measurements, the network can learn to predict future tidal current velocities with high accuracy. The real-time sensor inputs provide additional information that can improve the accuracy of the predictions, especially in the short term. For example, a sudden change in wind speed or water level can affect the tidal current velocity, and the LSTM network can incorporate this information to adjust its predictions accordingly. Other machine learning algorithms, such as autoregressive integrated moving average (ARIMA) models, can also be used to predict tidal current velocities, but they typically do not perform as well as LSTMs when there are complex, non-linear relationships in the data. Therefore, LSTMs are generally the preferred choice for short-term tidal current velocity prediction due to their ability to capture temporal dependencies and long-range patterns in sequential data.