Significant wave height forecasting is a key problem in ocean data analytics. Predicting the significant wave height is crucial for estimating the energy production from waves. Moreover, the timely prediction of large waves is important to ensure the safety of maritime operations, e.g. passage of vessels. We frame the task of predicting extreme values of significant wave height as an exceedance probability forecasting problem. Accordingly, we aim at estimating the probability that the significant wave height will exceed a predefined threshold. This task is usually solved using a probabilistic binary classification model. Instead, we propose a novel approach based on a forecasting model. The method leverages the forecasts for the upcoming observations to estimate the exceedance probability according to the cumulative distribution function. We carried out experiments using data from a buoy placed in the coast of Halifax, Canada. The results suggest that the proposed methodology is better than state-of-the-art approaches for exceedance probability forecasting.

Machine learning methods for the construction of data-driven reduced order model models are used in an increasing variety of engineering domains, especially as a supplement to expensive computational fluid dynamics for design problems. An important check on the reliability of surrogate models is Uncertainty Quantification (UQ), a self assessed estimate of the model error. Accurate UQ allows for cost savings by reducing both the required size of training data sets and the required safety factors, while poor UQ prevents users from confidently relying on model predictions. We examine several machine learning techniques, including both Gaussian processes and a family UQ-augmented neural networks: Ensemble neural networks (ENN), Bayesian neural networks (BNN), Dropout neural networks (D-NN), and Gaussian neural networks (G-NN). We evaluate UQ accuracy (distinct from model accuracy) using two metrics: the distribution of normalized residuals on validation data, and the distribution of estimated uncertainties. We apply these metrics to two model data sets, representative of complex dynamical systems: an ocean engineering problem in which a ship traverses irregular wave episodes, and a dispersive wave turbulence system with extreme events, the Majda-McLaughlin-Tabak model.

Marine litter poses significant threats to marine and coastal environments, with its impacts ever-growing. Remote sensing provides an advantageous supplement to traditional mitigation techniques, such as local cleaning operations and trawl net surveys, due to its capabilities for extensive coverage and frequent observation. In this study, we used Radiative Transfer Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of the Sentinel-2 mission in combination with machine learning algorithms. Our aim was to study the spectral behavior of marine plastic pollution and evaluate the applicability of RTMs within this research area. The results from the exploratory analysis and unsupervised classification using the KMeans algorithm indicate that the spectral behavior of pollutants is influenced by factors such as the type of polymer and pixel coverage percentage. The findings also reveal spectral characteristics and trends of association and differentiation among elements. The applied methodology is strongly dependent on the data, and if reapplied in new, more diverse, and detailed datasets, it can potentially generate even better results. These insights can guide future research in remote sensing applications for detecting marine plastic pollution.

First Coast Guard District Rear Admiral John Mauger offers his condolences to the loved ones of the Titan submersible crew on'America Reports.' BOSTON – EXCLUSIVE: Stockton Rush, the 61-year-old adventurer and CEO who died this week along with four other crew members in a catastrophic implosion near the bow of the Titanic, appeared in a series of never-before-seen surreal images captured during testing of the vehicle years ago. In the series of May 2018 photos taken in Abaco, Bahamas, and obtained by Fox News Digital, Rush can be seen peering through the vessel's lone porthole, testing out computer equipment inside and posing next to the 21-foot submersible on the deck of a ship before the test run. They were captured by underwater photographer Becky Kagan Schott, who said Rush had tested the vehicle numerous times in the area, and she befriended the adventurer. The Titan was designed to reach depths of 4,000 meters, according to OceanGate, the company Rush founded in 2009. It was meant for a variety of purposes, including scientific research, media production, and site surveying.

A former OceanGate Expeditions customer who took a trip to see the Titanic wreckage two years ago described the dive as a "kamikaze operation." An international search and rescue operation is ongoing for five crew members on OceanGate's Titan sub, which went missing Sunday on a planned deep sea tourist expedition. Arthur Loibl, a retired German businessman and adventurer who went on the same trip in 2021, shared his experience with OceanGate in an interview with The Associated Press. "You have to be a little bit crazy to do this sort of thing," Loibl said. He explained that the idea of touring the Titanic wreckage came to him on a trip to the South Pole in 2016.

While ROVs vary in design and capability, they can generally travel much deeper than manned vessels, Englot said. "Those kind of vehicles usually have robotic arms that are capable of carrying a payload, grasping an object, grabbing and turning a knob or a valve or something like that," he added. As of Thursday morning, several with the ability to reach the ocean floor had been deployed in the Atlantic as the Titan's estimated initial supply of 96 hours of oxygen dwindled – including the Victor 6000, which descended from the French L'Atalante research vessel to the ocean floor. File image of an asset of the rescue efforts – the Victor 6000 – an unmanned French robot which can dive up to 6,000 metres. It has arms that can be remotely controlled to cut cables or otherwise help release a stuck vessel. However, it does not have the capability of lifting the submersible on its own.

Dik Barton, the first British man to dive to the Titanic wreck, speculates what could have happened to the Titan submersible missing in the North Atlantic. The U.S. Coast Guard announced Thursday that the Canadian vessel Horizon Arctic deployed a remotely operated vehicle (ROV) "that has reached the sea floor and began its search" for the missing OceanGate Titan submarine. It is the first time during the search that a vessel is combing the floor of the Atlantic Ocean for the missing vessel and its five passengers. Previous search efforts have involved the use of aircraft and sonar. "The French vessel L'Atalante is preparing their ROV to enter the water," the Coast Guard also said.

Fox News correspondent Molly Line has more on the search to rescue the five individuals on the Titanic voyage on'Special Report.' BOSTON – Three new vessels arrived "on-scene" in the Atlantic Ocean Wednesday morning to join search and rescue efforts for the missing OceanGate Titan sub as the estimated oxygen supply on board continues to dwindle. The U.S. Coast Guard said the new vessels bring additional tools to scan the ocean floor as they race against the clock to save the five people onboard: OceanGate CEO Stockton Rush, British businessman Hamish Harding, father-and-son Shahzada and Suleman Dawood, who are members of one of Pakistan's wealthiest families, and Paul-Henry Nargeolet, a former French navy officer and leading Titanic expert. "The John Cabot has side-scanning sonar capabilities and is conducting search patterns alongside the Skandi Vinland and the Atlantic Merlin," the Coast Guard said. The John Cabot is a Canadian coast guard vessel, the Atlantic Merlin is a Canadian remotely operated vehicle (ROV), and the Skandi Vinland is a commercial ROV, authorities said.

Russia has accused Ukraine of planning to attack annexed Crimea with long-range United States and British missiles and warned it would retaliate if that happened. Russian Defence Minister Sergey Shoigu told a meeting of military officials on Tuesday that Moscow possesses information that Ukraine plans to strike Crimea with US-supplied HIMARS long-range rocket systems and British-supplied Storm Shadow cruise missiles. "The use of these missiles outside the zone of our special military operation would mean that the United States and Britain would be fully dragged into the conflict and would entail immediate strikes on decision-making centres in Ukraine," Shoigu said. Russia annexed Ukraine's Crimean Peninsula in 2014 and considers it to be outside the scope of its invasion – which is focused in eastern and southern Ukraine, where Ukraine is fighting to retake territory. Kyiv, which says it is battling for its survival in a war of colonial conquest, said it wants to reclaim all of its territory, including Crimea, the home of Russia's Black Sea naval base.

Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques to estimate the parameters that vary spatially in the covariance function. The selection of partitions is an important consideration, but it is often subjective and lacks a data-driven approach. To address this issue, in this study, we utilize the power of Convolutional Neural Networks (ConvNets) to derive subregions from the nonstationary data. We employ a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. In order to distinguish between stationary and nonstationary random fields, we conducted training on ConvNet using various simulated data. These simulations are generated from Gaussian processes with Mat\'ern covariance models under a wide range of parameter settings, ensuring adequate representation of both stationary and nonstationary spatial data. We assess the performance of the proposed method with synthetic and real datasets at a large scale. The results revealed enhanced accuracy in parameter estimations when relying on ConvNet-based partition compared to traditional user-defined approaches.