Abstract
Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.
Highlights
A better understanding of the environment is of vital importance for science, contributing to more efficient exploitation of natural resources and to the development of new strategies aimed at its protection
Some specific examples of the diversity of fields in which meteorological data can be used in are, among others: global solar radiation estimation [1], directional analysis of sea storms [2], estimation of hybrid energy systems taking into account economic and environmental objectives [3], wind power ramp events prediction [4], sea surface temperature prediction [5], study of the responses exhibited by plankton to fluid motions [6], trends in solar radiation [7] or simulation of extreme near shore sea conditions [8]
Studies on marine energy using Machine Learning (ML) and Soft Computing (SC) methodologies apply specific algorithms on data using custom-made implementations or scripts developed in some programming language; but they do not allow for building datasets in an automated way ready to be used as input for prediction tasks
Summary
A better understanding of the environment is of vital importance for science, contributing to more efficient exploitation of natural resources and to the development of new strategies aimed at its protection. As a support to traditional study procedures, SC and ML techniques [35,36] are being widely used in numerous research fields related to classification, regression, Energies 2021, 14, 468 and optimisation tasks, obtaining significant improvements in the performance of the results, either in engineering [10], energy, or environmental problems [37,38,39]. If more than one source of information is used to achieve a better characterisation of the problem under study [47,48,49], a data integration process, denominated as the matching process in this document, has to be carried out by researchers to manually create the datasets with the needed information Given that such process is of great relevance and has an extensive casuistry, the present work has been specially focused on it.
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