ABSTRACT The accurate modelling and prediction of air temperature values is an exceptionally important meteorological variable that affects in many areas. The present study is aimed at developing models for the prediction of monthly mean air temperature values in Turkey using ANN, ANFIS and SVMr methods. In developing the models, the monthly data derived from eight stations of the TSMS for the 1963–2015 period were used, including latitude, longitude, elevation, month, and minimum, maximum and mean air temperatures. The performances of the ANN, ANFIS and SVMr models were compared using R2, MSE, MAPE and RRMSE. In order to verify the differences between the predicted temperature values provided by the ANN, ANFIS and SVMr models and the observed temperature values derived from the stations, a t-test analysis was conducted, and the best ANN, ANFIS and SVMr models were determined according to the statistical performance values. These models were then used to make air temperature predictions for the cities. Manova was carried out to determine the effects of the differences temperature predictions and RRMSE values of the models. Generally, the statistical performance values of the ANFIS models were found to be slightly better than those of the ANN and SVMr models.

中文翻译：

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使用人工神经网络、自适应神经模糊推理系统和支持向量回归方法模拟月平均气温：以土耳其为例

摘要 空气温度值的准确建模和预测是影响许多领域的极其重要的气象变量。本研究旨在开发使用 ANN、ANFIS 和 SVMr 方法预测土耳其月平均气温值的模型。在开发模型时，使用了 1963-2015 年期间 TSMS 八个站的月度数据，包括纬度、经度、海拔、月份以及最低、最高和平均气温。使用 R2、MSE、MAPE 和 RRMSE 比较了 ANN、ANFIS 和 SVMr 模型的性能。为了验证由 ANN、ANFIS 和 SVMr 模型提供的预测温度值与来自站点的观测温度值之间的差异，进行了 t 检验分析，并且最佳 ANN，根据统计性能值确定ANFIS和SVMr模型。然后使用这些模型对城市进行气温预测。进行 Manova 以确定差异温度预测和模型的 RRMSE 值的影响。一般而言，发现ANFIS 模型的统计性能值略好于ANN 和SVMr 模型的统计性能值。