EXTRACTION OF RULES BY CLASSIFICATION FROM WEATHER STATION DATA TO HELP IN THE FORECAST OF TEMPERATURE AND HUMIDITY INDEX FOR DAIRY CATTLE - dos Santos, M. P.; do Vale, M. M.; Santos, J. P. A.; dos Santos, J. C. (Revista Brasileira de Engenharia de Biossistemas, 2014)
Forecasts for comfort index to dairy cattle are unavailable in Brazil and the extraction rules on weather behavior can assist in predicting the animal’s comfort, especially for those who are in unprotected places. This study aims to develop a methodology for extracting predictive rules from heat stress conditions in dairy cattle. The analysis was performed using the database of the National Institute of Meteorology (INMET), referring to the hourly averages for the period between September 15th to November 13th of 2013 in Santa Maria - RS, Brazil. The input variables were time of day, air temperature, dew point temperature, relative humidity and the temperature and humidity index. The extraction of the rules was done by the technique of Data Mining and the classification task by building the J48 decision tree algorithm. The classification of the Temperature and Humidity Index (THI) was based on two classes, being NORMAL for THI values ​​less than or equal to 74, and ALERT to values ​​above 74, considered as a promoter of stress. Data mining has resulted in the description of 11 rules of the relationship between temperature, relative humidity and time of day with the THI. Data mining has enabled the understanding of the variables analyzed and the generated rules can help in forecasts based on meteorological forecasts and environmental temperature controllers and relative humidity schedule.