The accurate forecast of air temperature plays an important role in water resources management, land–atmosphere interaction, and agriculture. However, it is difficult to accurately predict air temperature due to its non-linear and chaotic nature. Several deep learning techniques have been proposed over the last few decades to forecast air temperature. This study provides a comprehensive review of artificial neural network (ANN)-based approaches (such as recurrent neural network (RNN), long short-term memory (LSTM), etc.), which were used to forecast air temperature. The focus is on the works during 2005–2020. The review shows that the neural network models can be employed as promising tools to forecast air temperature. Although the ANN-based approaches have been utilized widely to predict air temperature due to their fast computing speed and ability to deal with complex problems, no consensus yet exists on the best existing method. Additionally, it is found that the ANN methods are mainly viable for short-term air temperature forecasting. Finally, some future directions and recommendations are presented.

中文翻译：

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神经网络在气温预测中的应用

气温的准确预测在水资源管理，地-气相互作用和农业中发挥着重要作用。然而，由于其非线性和混乱的性质，难以准确地预测空气温度。在过去的几十年中，已经提出了几种深度学习技术来预测气温。这项研究对基于人工神经网络（ANN）的方法（例如递归神经网络（RNN），长期短期记忆（LSTM）等）进行了全面综述，这些方法可用于预测气温。重点是2005–2020年期间的工作。综述表明，神经网络模型可以用作预测气温的有前途的工具。尽管基于ANN的方法由于其快速的计算速度和处理复杂问题的能力而已被广泛用于预测气温，但对于最佳的现有方法尚无共识。此外，发现人工神经网络方法主要适用于短期气温预测。最后，提出了一些未来的方向和建议。