Generación de pronósticos para la precipitación diaria en una serie de tiempo de datos meteorológicos

Meteorological time series forecasts can help decision-making processes carried out by entities in charge of disaster prevention and early warning generation before the possibility of natural events involving situations which are dangerous for communities. There are a considerable number of methods...

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Autores:: Hernández-Leal, Emilcy Juliana
Duque-Méndez, Néstor Darío
Moreno-Cadavid, Julián

Tipo de recurso:

Fecha de publicación:: 2016

Institución:: Universidad Santo Tomás

Repositorio:: Repositorio Institucional USTA

Idioma:: spa

Description
Summary:	Meteorological time series forecasts can help decision-making processes carried out by entities in charge of disaster prevention and early warning generation before the possibility of natural events involving situations which are dangerous for communities. There are a considerable number of methods for these forecasts, ranging from simplistic or Naïve methods to those which employ more complex techniques such as those using artificial intelligence. This experimental study worked with a meteorological time series from the station agronomy in the city of Manizales, which provides data on the variables: precipitation, average temperature, sunlight and relative humidity. Forecasts were employed with the Naïve approach, with artificial neuronal networks and with neuro-fuzzy networks; also comparing these with a multiple linear regression, with the goal of verifying their precision. The results obtained in this study show firstly that it is possible to refine the models generally used in order to achieve more conclusive results and secondly that they can be extended to other monitoring stations in the region, including new variables, both explanatory and predictive.

Generación de pronósticos para la precipitación diaria en una serie de tiempo de datos meteorológicos

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