scholarly journals Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 468
Author(s):  
Antonio Manuel Gómez-Orellana ◽  
Juan Carlos Fernández ◽  
Manuel Dorado-Moreno ◽  
Pedro Antonio Gutiérrez ◽  
César Hervás-Martínez
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Soft Computing ◽  
Energy Flux ◽  
Meteorological Data ◽  
Software Tool ◽  
Quality Data ◽  
Coastal Protection ◽  
Ocean Engineering

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.

scholarly journals An urban big data-based air quality index prediction: A case study of routes planning for outdoor activities in Beijing

2019 ◽  
Vol 47 (6) ◽  
pp. 948-963
Author(s):  
Zhiqiang Zou ◽  
Tao Cai ◽  
Kai Cao
Keyword(s):  
Big Data ◽  
Air Quality ◽  
Quality Index ◽  
Meteorological Data ◽  
Air Quality Index ◽  
Quality Data ◽  
Long Distance ◽  
Outdoor Activities ◽  
Proposed Model

Urban big data include various types of datasets, such as air quality data, meteorological data, and weather forecast data. Air quality index is broadly used in many countries as an indicator to measure the air pollution status. This indicator has a great impact on outdoor activities of urban residents, such as long-distance cycling, running, jogging, and walking. However, for routes planning for outdoor activities, there is still a lack of comprehensive consideration of air quality. In this paper, an air quality index prediction model (namely airQP-DNN) and its application are proposed to address the issue. This paper primarily consists of two components. The first component is to predict the future air quality index based on a deep neural network, using historical air quality datasets, current meteorological datasets, and weather forecasting datasets. The second component refers to a case study of outdoor activities routes planning in Beijing, which can help plan the routes for outdoor activities based on the airQP-DNN model, and allow users to enter the origin and destination of the route for the optimized path with the minimum accumulated air quality index. The air quality monitoring datasets of Beijing and surrounding cities from April 2014 to April 2015 (over 758,000 records) are used to verify the proposed airQP-DNN model. The experimental results explicitly demonstrate that our proposed model outperforms other commonly used methods in terms of prediction accuracy, including autoregressive integrated moving average model, gradient boosted decision tree, and long short-term memory. Based on the airQP-DNN model, the case study of outdoor activities routes planning is implemented. When the origin and destination are specified, the optimized paths with the minimum accumulated air quality index would be provided, instead of the standard static Dijkstra shortest path. In addition, a Web-GIS-based prototype has also been successfully developed to support the implementation of our proposed model in this research. The success of our study not only demonstrates the value of the proposed airQP-DNN model, but also shows the potential of our model in other possible extended applications.

MACHINE LEARNING FOR SOFTWARE

2019 ◽  
pp. 251-255
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Soft Computing ◽  
Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

THE VALUE OF DATA INTEGRATION IN A COMPLEX WELL SCENARIO: A CASE STUDY FROM THE WEST NETHERLANDS

2020 ◽  
Author(s):  
Abhinandan Kohli ◽  
◽  
Emile Fokkema ◽  
Oscar Kelder ◽  
Zulkifli Ahmad ◽  
...  
Keyword(s):  
Data Integration ◽  
The West ◽  
Complex Well

scholarly journals Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack
Keyword(s):  
Wind Turbine ◽  
Western Europe ◽  
Meteorological Data ◽  
Turbine Blades ◽  
Weather Conditions ◽  
Annual Rainfall ◽  
Wind Turbine Blades ◽  
Weather Patterns ◽  
The Uk

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

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