Predictive models of minimum temperatures for the south of Buenos Aires province

Abstract

Depending on the time of development of a crop temperature below 0 °C can cause damage to the plant, altering its development and subsequent yield. Since frosts are identified from the minimum air temperature, the objective of this research paper is to generate forecast -(predictive) models at 1, 3 and 5 days of the minimum daily temperature (Tmin) for Bahía Blanca city. Non-linear numerical models are generated using artificial neural networks and geometric models of finite elements. Six independent variables are used: temperature and dew point temperature at meteorological shelter level, relative humidity, cloudiness observed above the station, wind speed and direction measured at 10 m altitude. Data have been obtained between May and September from 1956 to 2015. Once the available data had been analyzed, this period was reduced to 2007–2015. For the selection of the most suitable model, the correlation coefficient of Pearson (R), the determination coefficient (R2) and the Mean Absolute Error (MAE) are evaluated. The results of the study determine that the geometric model of finite elements with 4 variables, over 9 years (2007–2015) and separated by the season of the year is the one that presents better adjustment in the forecast of Tmin with up to 5 days of anticipation.