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Temperature Prediction Based on Integrated Deep Learning and Attention Mechanism

机译：基于集成深度学习和关注机制的温度预测

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It is greatly significant to predict air temperature accurately for effective warning of extreme weather events, whereas the complex and nonlinear characteristics of meteorological data make this kind of forecast difficult to achieve high accuracy. To deal with this issue, a novel model named CNN-GRU-RPASM (Convolution Neural Networks - Gated Recurrent Unit - Relative Position-based Self-Attention Mechanism) was proposed in this paper. Apart from the traditional counterparts, the CNN-GRU-RPASM model innovatively combines the advantages of CNN and GRU, and introduces a gaussian amplifier model to improve the self-attention mechanism with relative position information. Firstly, CNN was used to extract the characteristics of the meteorological input data. Then, the improved self-attention mechanism was employed to extract key information from the data sequence. And finally, GRU was utilized to encode the relationship information among time-series data. The performance evaluation with the real meteorological data shows that the CNN-GRU-RP ASM model performs better than its traditional counterparts. This new model will be deployed in the agricultural production service system to provide technical supports for extreme weather disaster warning forecasting.
机译：准确地预测气温以获得极端天气事件的有效警告是非常重要的，而气象数据的复杂和非线性特征使这种预测难以实现高精度。为了处理这个问题，本文提出了一种名为CNN-GRU-RPASM（卷积神经网络门控复发单元 - 相对位置基自我关注机制）的新型模型。除了传统的同行，CNN-GRU-RPASM模型的创新性地结合了CNN和GRU的优点，并引入了高斯放大器模型，以改善具有相对位置信息的自我关注机制。首先，CNN用于提取气象输入数据的特征。然后，采用改进的自我注意机制来从数据序列中提取密钥信息。最后，利用GRU编码时间序列数据之间的关系信息。具有实际气象数据的性能评估表明，CNN-GRU-RP ASM模型比其传统对应物更好。该新模式将部署在农业生产服务系统中，为极端天气灾害预测提供技术支持。

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