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Bibliographic Review on Data Mining Techniques Used with Weather Data

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Abstract

This paper describes an exhaustive bibliographic review, which searches for and analyzes the latest trends in the use of techniques and algorithms of a Data Mining (DM) process, focused on the context of meteorological data manipulation, its relationship with electrical and vehicular traffic data. For this, the Systematic Mapping Study (SMS) methodology is used, which is applied for the period from 2015 to 2019. This study is carried out mainly by monitoring the results of the search for scientific articles, regarding three stages of a DM process, these are: data preparation, modeling, and evaluation stage. In the results obtained in this study, it’s observed that, in the primary works analyzed, they tend to present more detailed information on the DM techniques used in the modeling stage. Second, works related to the evaluation stage continues. Finally, data preparation stage is where the least amount of information on the techniques used is provided. For the context established as the interest of this study, it’s possible to identify techniques most used in recent years, which correspond to Artificial Neural Networks (ANN), Vector Support Machines (VSM), and clusters. The results of the preliminary study allow to establish the conceptual bases necessary to deepen the appropriate DM techniques for working with climatic data associated with time series records. integrated with data on electricity production and vehicular traffic, in order to obtain more efficient models for use cases.

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    Wilson Castillo-Rojas & César Hernández

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Castillo-Rojas, W., Hernández, C. Bibliographic Review on Data Mining Techniques Used with Weather Data. Program Comput Soft 47, 817–829 (2021). https://doi.org/10.1134/S0361768821080090

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