A visual big data system for the prediction of weather-related variables: Jordan-Spain case study
Pages 13103 - 13139
Abstract
The Meteorology is a field where huge amounts of data are generated, mainly collected by sensors at weather stations, where different variables can be measured. Those data have some particularities such as high volume and dimensionality, the frequent existence of missing values in some stations, and the high correlation between collected variables. In this regard, it is crucial to make use of Big Data and Data Mining techniques to deal with those data and extract useful knowledge from them that can be used, for instance, to predict weather phenomena. In this paper, we propose a visual big data system that is designed to deal with high amounts of weather-related data and lets the user analyze those data to perform predictive tasks over the considered variables (temperature and rainfall). The proposed system collects open data and loads them onto a local NoSQL database fusing them at different levels of temporal and spatial aggregation in order to perform a predictive analysis using univariate and multivariate approaches as well as forecasting based on training data from neighbor stations in cases with high rates of missing values. The system has been assessed in terms of usability and predictive performance, obtaining an overall normalized mean squared error value of 0.00013, and an overall directional symmetry value of nearly 0.84. Our system has been rated positively by a group of experts in the area (all aspects of the system except graphic desing were rated 3 or above in a 1–5 scale). The promising preliminary results obtained demonstrate the validity of our system and invite us to keep working on this area.
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© Springer Science+Business Media, LLC, part of Springer Nature 2020.
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Kluwer Academic Publishers
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Publication History
Published: 01 October 2020
Accepted: 09 September 2020
Revision received: 30 July 2020
Received: 07 May 2020
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