Air temperature estimation techniques in Minas Gerais state, Brazil, Cwa and Cwb climate regions according to the Köppen-Geiger climate classification system

Abstract

Air temperature significantly affects the processes involving agricultural and human activities. The knowledge of the temperature of a given location is essential for agricultural planning. It also helps to make decisions regarding human activities. However, it is not always possible to determine this variable. It is necessary to make a precise estimate, using methods that are capable of detecting the existing variations. The aim of this study was to develop models of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) to estimate the mean (Tmean), maximum (Tmax), and minimum (Tmin) monthly air temperatures as a function of geographic coordinates and altitude for different localities in Minas Gerais state, Brazil, with climatic classification Cwa or Cwb. The average monthly data (Tmean, Tmax, and Tmin), over a period of 30 years, were collected from 20 climatological stations. The MLR was able to estimate the Tmax with accuracy. However, the predictive capacity of estimating Tmean and Tmin was low. The algorithms RF and ANN were used to estimate Tmean, Tmax, and Tmin with high accuracy. The best results were obtained using the RF model.