Air Temperature (AT) is a crucial parameter for many disciplines such as hydrology, irrigation, ecology and agriculture. In this respect, accurate AT prediction is required for applications related to agricultural operations, energy generation, traveling, human and recreational activities. In this study, four different machine learning approaches such as Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fuzzy C-Means (FCM), ANFIS with Subtractive Clustering (SC) and ANFIS with Grid Partition (GP) and Long Short-Term Memory (LSTM) neural network were used to make one-hour ahead and one-day ahead short-term AT predictions. Concerning the test site, the measured AT data were obtained from a solar power plant installed in the city of Tarsus, Turkey. Correlation coefficient (R), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used as quality metrics for prediction. Predicted values of the LSTM, ANFIS-FCM, ANFIS-SC and ANFIS-GP models were compared with the observed values by evaluating their prediction errors. According to the hourly AT prediction, the RMSE values in the testing process were found to be 0.644 (°C), 0.721 (°C), 0.722 (°C) and 0.830 (°C) for the LSTM, ANFIS-FCM, ANFIS-SC and ANFIS-GP models, respectively. On the other hand, the RMSE values of the corresponding methods for daily AT prediction were obtained as 1.360 (°C), 1.366 (°C), 1.405 (°C) and 1.905 (°C), respectively. The comparison of hourly and daily prediction results revealed that the LSTM neural network provided the highest accuracy results in both one-hour ahead and one-day ahead short-term AT predictions, and mainly presented higher performance than all ANFIS models.

气温（AT）是许多学科（例如水文学，灌溉，生态学和农业）的关键参数。在这方面，与农业运营，能源生产，旅行，人类和娱乐活动有关的应用需要准确的AT预测。在这项研究中，四种不同的机器学习方法，例如具有模糊C均值（FCM）的自适应神经模糊推理系统（ANFIS），具有减法聚类（SC）的ANFIS和具有网格划分（GP）和长短期记忆的ANFIS （LSTM）神经网络用于进行短期AT预测提前1小时和提前1天。关于测试现场，测得的AT数据是从安装在土耳其塔尔苏斯市的太阳能发电厂获得的。相关系数（R），平均绝对误差（MAE）和均方根误差（RMSE）用作预测的质量指标。通过评估它们的预测误差，将LSTM，ANFIS-FCM，ANFIS-SC和ANFIS-GP模型的预测值与观测值进行比较。根据每小时的AT预测，对于LSTM，ANFIS-FCM，ANFIS，测试过程中的RMSE值分别为0.644（°C），0.721（°C），0.722（°C）和0.830（°C）。 -SC和ANFIS-GP模型。另一方面，用于每日AT预测的相应方法的RMSE值分别为1.360（°C），1.366（°C），1.405（°C）和1.905（°C）。每小时和每日预测结果的比较表明，LSTM神经网络在提前1小时和提前1天的短期AT预测中均提供了最高的准确度结果，