Air temperature is a vital meteorological variable required in many applications, such as agricultural and soil sciences, meteorological and climatological studies, etc. Given the importance of this variable, this study seeks to estimate minimum (T_min), maximum (T_max), and mean (T) air temperatures by applying a linear autoregressive (AR) time series model and then developing a hybrid model by means of coupling the AR and a non-linear time series model, namely autoregressive conditional heteroscedasticity (ARCH). Hence, the hybrid AR-ARCH model was tested. To that end, the T_min, T_max, and T data from 1986 to 2015 at two weather stations located in Northwestern Iran were used for both daily and monthly time scales. The results showed that the hybrid time series model (i.e., AR-ARCH) performed better than the single AR for estimating the air temperature parameters at the study sites. Multi-layer perceptron (MLP) was then employed to estimate the air temperatures using lagged temperature data as input predictors. Next, the single AR and hybrid AR-ARCH time series models were utilized to implement the hybrid MLP-AR and MLP-AR-ARCH models. It is worth noting that developing the hybrid MLP-AR and MLP-AR-ARCH models, as well as AR-ARCH one is the novelty of this study. Three statistical metrics including root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (NRMSE) were used to investigate the performance of whole the developed models. The hybrid MLP-AR and MLP-AR-ARCH models were found to perform better than the single MLP when estimating the daily and monthly T_min, T_max, and T; however, the MLP-AR models outperformed the MLP-AR-ARCH ones. At the end of this study, the performance of MLP was evaluated under an external condition (i.e., estimating the temperature components at any particular site using the temperature data of an adjacent location). The results indicated that the temperature data of a nearby station can be used for estimating the temperatures of a desired station. Most accurate results during the test stage were obtained under a local assessment through the hybrid MLP-AR(1) at the Tabriz station when estimating the monthly T_max (RMSE = 0.199 °C, MAE = 0.159 °C, NRMSE = 1.012%) and hybrid MLP-AR(12) at the Urmia station when estimating the daily T_max (RMSE = 0.364 °C, MAE = 0.277 °C, NRMSE = 1.911%).

空气温度是许多应用（例如农业和土壤科学，气象和气候研究等）中所需的重要气象变量。鉴于此变量的重要性，本研究旨在估算最小值（T _min），最大值（T _max），通过应用线性自回归（AR）时间序列模型，然后通过将AR和非线性时间序列模型耦合来开发混合模型，即自回归条件异方差（ARCH），来确定平均（T）气温。因此，测试了混合AR-ARCH模型。为此，T _min，T _max，以及1986年至2015年位于伊朗西北部两个气象站的T数据分别用于每日和每月时间尺度。结果表明，混合时间序列模型（即AR-ARCH）在估计研究地点的空气温度参数方面比单一AR表现更好。然后使用多层感知器（MLP）将滞后的温度数据用作输入预测变量来估计空气温度。接下来，利用单个AR和混合AR-ARCH时间序列模型来实现混合MLP-AR和MLP-AR-ARCH模型。值得注意的是，开发混合MLP-AR和MLP-AR-ARCH模型以及AR-ARCH之一是这项研究的新颖之处。三种统计指标，包括均方根误差（RMSE），平均绝对误差（MAE），并使用归一化RMSE（NRMSE）来研究整个开发模型的性能。在估计每日和每月的T值时，发现混合MLP-AR和MLP-AR-ARCH模型的性能优于单个MLP。_min，T _max和T; 但是，MLP-AR模型优于MLP-AR-ARCH模型。在本研究结束时，在外部条件下评估了MLP的性能（即，使用相邻位置的温度数据估算任何特定位置的温度分量）。结果表明，附近站点的温度数据可用于估计所需站点的温度。在估算每月的T _max（RMSE = 0.199°C，MAE = 0.159°C，NRMSE = 1.012％）时，通过大不里士（Tabriz）站的混合MLP-AR（1）在本地评估下获得了测试阶段最准确的结果。估计每日_最大温度时，在Urmia站的混合MLP-AR（12） （RMSE = 0.364°C，MAE = 0.277°C，NRMSE = 1.911％）。