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This paper develops a probabilistic model to predict the tidal current for modeling the prediction uncertainty, and thereby the forecast error. This requires the extension of the deterministic models from a point-by-point forecast to the probabilistic models with prediction intervals (PIs). The proposed model uses PIs to construct the bandwidth, which models the uncertainty of tidal current prediction properly. It uses the lower upper bound estimation method to train the neural network (NN) without making any assumption about the distribution of the forecast error. In order to adjust the weighting and biasing factors of NN, firefly algorithm with a new two-phase modification method is developed to search the problem space globally. Two benchmarks are used to show the search ability of the algorithm. The high accuracy of the proposed model is examined on the practical tidal data collected from the Bay of Fundy, NS, Canada. [ABSTRACT FROM AUTHOR]

Tide refers to a phenomenon that causes the change of water level in oceans. Tidal level forecasting plays an important role in many real-world applications especially those related to oceanic and coastal areas. For instance, accurate forecasting of tidal level can significantly increase the vessels’ safety as an excessive level of tidal makes serious problems in the movement of vessels. In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and nonparametric approach termed as the lower upper bound estimation (LUBE) model. Moreover, we develop a novel deep neuroevolution algorithm based on a two-stage modification of the gaining-sharing knowledge optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. This leads to a decline in the complexity raises in designing manually the deep learning architectures, as well as an enhancement in the performance of the PIs. We also utilize coverage width criterion to establish an excellent correlation appropriately between both the PI coverage probability and PI normalized average width. We indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada. [ABSTRACT FROM AUTHOR]

Presents a study that characterized the sounds of North Atlantic right whales in the Bay of Fundy during the daytime in July 1999. Method used to discriminate whale sounds from background noise; Average duration of the sound; Time intervals between consecutively received sounds.

Introduces a special issue on marine mammals and noise. Characterization of North Atlantic right-whale sounds in the Bay of Fundy; Efforts exerted to protect marine mammals; Significance of marine mammals research.