This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.

With the model size I've selected it looks like we could stop with around 15 epochs as there is no further improvement. Model's accuracy is between 0.8 and 0.85.

Spanish treasure fleets that traversed the Atlantic Ocean to the Americas and back were a 16th-century invention as important as free two-day shipping. Organized 70 years after Columbus's first voyage, the fleet was made up of several specialized ships with one primary goal: Exploiting the riches of the New World as efficiently as possible. The San José, the largest galleon and the flagship of one group of Spanish ships that started sailing in the 16th century, was big and -- thanks to 62 bronze cannons engraved with dolphins -- deadly enough to deter or destroy ships, whether pirates or rival nations. On June 8, 1708, during the War of the Spanish Succession, the San José's gunpowder ignited during a battle with British ships, sending 600 sailors to the bottom of the Atlantic Ocean -- along with gold, silver and emeralds from mines in Peru, a total haul valued at some $17 billion in today's dollars. It stands as one of the most expensive maritime losses in history.

A Spanish galleon laden with treasures worth £12.6 billion ($17 billion) that sank to the bottom of the Caribbean 300 years ago was found using an autonomous robot, researchers have revealed. The San Jose, sunk by the Royal Navy, gained a reputation as the'holy grail' of shipwrecks and was carrying one of the most valuable hauls of treasure ever lost at sea. The 62-gun, three-masted galleon, went down on June 8, 1708, with 600 people on board as well as a treasure of gold, silver and emeralds during a battle with British ships in the War of Spanish Succession. The San Jose was located by an underwater autonomous vehicle operated by the Woods Hole Oceanographic Institution (WHOI) back in 2015. The institution said it was keeping its involvement in the discovery quiet out of respect for the Colombian government.

UAVs are tackling everything from disease control to vacuuming up ocean waste to delivering pizza, and more. Drone technology has been used by defense organizations and tech-savvy consumers for quite some time. However, the benefits of this technology extends well beyond just these sectors. With the rising accessibility of drones, many of the most dangerous and high-paying jobs within the commercial sector are ripe for displacement by drone technology. The use cases for safe, cost-effective solutions range from data collection to delivery. And as autonomy and collision-avoidance technologies improve, so too will drones' ability to perform increasingly complex tasks. According to forecasts, the emerging global market for business services using drones is valued at over $127B. As more companies look to capitalize on these commercial opportunities, investment into the drone space continues to grow. A drone or a UAV (unmanned aerial vehicle) typically refers to a pilotless aircraft that operates through a combination of technologies, including computer vision, artificial intelligence, object avoidance tech, and others. But drones can also be ground or sea vehicles that operate autonomously.

With plans for brand new megacities, allowing women to drive and foreign-run cinemas, Saudi Arabia's Crown Prince Mohammed bin Salman is on a charm offensive trying to promote his country as an international investment destination. The strategy aims at luring foreign money to help the world's biggest oil exporter create a new economy away from oil dependency in order to prevent future instability. On Wednesday, the International Monetary Fund (IMF) said Riyadh's break-even oil price for 2018 is likely to be around $88 a barrel. North Sea Brent is currently trading down around $74 a barrel. And although the oil price is up considerably from 2014, the director of the IMF's Middle East department Jihad Azour said the focus in Saudi needs to remain on economic and social reforms.

In the wake of the 2010 Deepwater Horizon disaster in the Gulf of Mexico, oceanographer Kaitlin Frasier of the University of California, San Diego, set out to assess the damage that the massive oil spill caused. "We needed to know what happened to marine mammals," she says. Specifically, Frasier was concerned with the spill's impact on dolphin populations. Trying to track the animals from the surface is expensive and time consuming, so Frasier used a different approach: deploying hydrophones to the seabed to passively record every sound in the ocean. By separating out dolphin vocalizations from the general thrum of ocean noise, Frasier hoped to detect trends in the animals' population density.

An autonomous robot will be deployed to an offshore oil and gas platform in the North Sea later this year, in a first for the sector. The £4m project's backers said the move was designed to take humans out of dangerous and dull jobs, and reinvent oil and gas as an industry of the future. Under the pilot scheme, the robot will initially be deployed at the French oil firm Total's gas plant on Shetland before being sent to join the 120 workers on the company's Alwyn platform, 440km north-east of Aberdeen. The machine, made by Austrian firm Taurob and supported on the software side by German university TU Darmstadt, will be used for visual inspections and detecting gas leaks. Rebecca Allison, asset integrity solution centre manager at the publicly-funded Oil and Gas Technology Centre, insisted autonomous robots would not be used to cut the wage burden of offshore workers who are paid a premium for working in tough, remote conditions.

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease Diplodia sapinea in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and RF (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data.

Gervais' beaked whales are easily one of the most elusive mammals to swim through our oceans. Most of the information we have about them comes from studies of corpses that have washed ashore, and the first live whale was only spotted about 20 years ago. On February 27, photographer and videographer Patrick Dykstra captured what may be the first drone or aerial footage of Gervais' beaked whales. He was filming about three miles off the west coast of Dominica in the Caribbean Sea. Dykstra and his Picture Adventure Expeditions team accidentally came across the rare beaked whales when they were filming sperm whales for an upcoming production.