Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works.

Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships’ environmental performance-related data, collected during ships’ voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships’ environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster.

The maritime is facing a gradual proliferation of data, which is frequently coupled with the presence of subpar information that contains missing and duplicate data, erroneous records, and flawed entries as a result of human intervention or a lack of access to sensitive and important collaborative information. Data limitations and restrictions have a crucial impact on inefficient data-driven decisions, leading to decreased productivity, augmented operating expenses, and the consequent substantial decline in a competitive edge. The missing or inadequate presentation of significant information, such as the vessel’s primary engine model, critically affects its capabilities and operating expenses as well as its environmental impact. In this study, a comprehensive study was employed, using and comparing several machine learning classification techniques to classify a ship’s main engine model, along with different imputation methods for handling the missing values and dimensionality reduction methods. The classification is based on the technical and operational characteristics of the vessel, including the physical dimensions, various capacities, speeds and consumption. Briefly, three dimensionality reduction methods (Principal Component Analysis, Uniform Manifold Approximation and Projection, and t-Distributed Stochastic Neighbor Embedding) were considered and combined with a variety of classifiers and the appropriate parameters of the dimensionality reduction methods. According to the classification results, the ExtraTreeClassifier with PCA with 4 components, the ExtraTreeClassifier with t-SNE with perplexity equal to 10 and 3 components, and the same classifier with UMAP with 10 neighbors and 3 components outperformed the rest of the combinations. This classification could provide significant information for shipowners to enhance the vessel’s operation by optimizing it.