ARTIFICIAL NEURAL NETWORKS FOR PREDICTING ANIMAL THERMAL COMFORT

ABSTRACT The objective of this study was to develop artificial neural networks (ANNs) for predicting animal thermal comfort based on temperature and relative humidity of the air for each day of the year. The data on temperature and relative humidity for a 25-year historical series collected at the Padre Ricardo Remetter Conventional Meteorological Station, located in the city of Santo Antônio de Leverger - Mato Grosso (Brazil), were retrieved from the website of the National Institute of Meteorology. According to the day of the year, the temperature and humidity index was determined as a function of the climatic variables. Therefore, the day of the year was the input variable of the neural networks, and the temperature and humidity index (THI) was the output variable. The number of layers and neurons used for establishing different architectures was variable. Data were adjusted on the basis of mean square errors, performance and efficiency indexes, and normality tests. The values estimated by the networks and those obtained from the historical series did not differ significantly. The networks with the best performance were selected for graphical analysis of residuals. The ANNs developed in this study predicted animal thermal comfort with adequate reliability and precision.

摘要 本研究旨在构建人工神经网络（Artificial Neural Networks，ANNs），基于全年每日的空气温度与相对湿度，预测动物热舒适度。研究数据取自位于巴西马托格罗索州圣安东尼奥-德莱韦尔热市的帕德里·里卡多·雷梅特常规气象站采集的25年历史气温与相对湿度序列，数据来源于巴西国家气象研究所官网。依据一年中的日期，结合气候变量计算得到温湿度指数（Temperature and Humidity Index，THI）。因此，人工神经网络的输入变量为一年中的日期，输出变量为温湿度指数（THI）。用于构建不同网络架构的层数与神经元数量均为可变参数。基于均方误差、性能与效率指标以及正态性检验对数据进行拟合调优。网络估算值与历史序列获取的实测值之间无显著差异。选取性能最优的网络开展残差图形分析。本研究构建的人工神经网络可对动物热舒适度实现可靠且精准的预测。