Resumen

HERNANDEZ VICTOR, Yoelkis; ALMEIDA MALDONADO, Enrique  y  BROWN MANRIQUE, Oscar. Use of data mining for the seasonal adjustment of the series of precipitation data in the municipality of Venezuela. RCCI [online]. 2023, vol.17, n.2  Epub 01-Dic-2023. ISSN 2227-1899.

Climate change, widely influenced by various identified phenomena, has seriously affected different sectors of society, threatening food security and productivity, taking actions to mitigate or adapt is vital in these times. Currently information and communications technologies play an important role in the extraction, transformation and loading of data, due to the large volumes of stored information. There are many variables that intervene in the identification of climatic changes in all sectors, specifically in the investigation, an analysis of chronological data series of daily rainfall in the municipality of Venezuela, Ciego de Ávila, is carried out, which must be seasonally adjusted to show an optimal result for any further analysis. Under this premise, the interest of this work is based on the construction of a software using the Python language and the Django framework that allows the chronological data series to be seasonally adjusted and to speed up and make decisions about climate variability using data modeling. To achieve the objective, the method of monthly averages is proposed. The official information is from the Institute of Meteorology of Ciego de Ávila, which provides for its use by researchers.

Palabras clave : Django; Precipitation; Climate Change; Venezuela.

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