Machine Learning (ML) techniques for time series prediction are becoming increasingly accurate and helpful, particularly in considering climate change. As more methods are developed, it follows that differentiating between them is becoming increasingly more important as well. This work took a local temperature time series as a dependent variable and a collection of relevant climatology time series as independent variables and applied leading Machine Learning methods to them. The six methods tested included four simple models: Linear Regression (L.R.), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), In addition of two ensemble model methods: Random Forest (R.F.) and Adaptive Boosting (AdB). Results compared all the method's training and predictive performances to evaluate the method's overall effectiveness in forecasting the average daily temperature value. Actual data was used to train each of the mentioned ML methods, and then they were used to predict the future temperature in the study area. The analysis revealed that out of the six methods tested, the Artificial Neural Network outperformed the others in both training and prediction of temperature values in the Memphis, TN climate.

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When considering the design of a ship, an important objective is to always try and develop one that allows for maximum cargo capacity with the lowest propulsion power requirement while providing a sufficient amount of strength and stability for its safe operation. The ship with the lowest propulsion power consumes the least amount of fuel and produces the lowest amount of exhaust gas that may be harmful to the environment. In some cases, the aerodynamic resistance can be neglected, but for a high speed vessel such as a modern containership, the air resistance can be in the range of 2% to 10% of the total resistance. Aerodynamic resistance can therefore have a significant effect on power requirements and is strongly influenced by the height, breadth, and the number of container stacks on the deck. The freeboard, beam of the ship, deck house design, ship speed, wind speed, and water flow direction will also contribute significantly to a ship's resistance and required propulsive power. This paper outlines the application of computational fluid dynamic simulation as a design tool to find a strategy for the optimal arrangement of the container stacks on deck so that the vessel uses the lowest effective propulsion power to achieve a fuel efficient ship. It is deduced that an optimal stack arrangement can reduce air resistance by about 30%.

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