基于注意力机制的LSTM和ARIMA集成方法在土壤温度中应用

Integrated LSTM and ARIMA method based on attention modelfor soil temperature

为准确分析土壤温度特性问题,提出了基于注意力机制的多通道长短期记忆网络(LSTM)融合ARIMA算法的预测模型。通过提取长短期不同时刻重要时间特征,并利用ARIMA时间序列模型提取线性特征优势更准确预测土壤温度。为验证该模型,本文在瑞士两个气象站(Laegern和Fluehli气象站)测试了未来6、12和24 h内,同时间土壤深度5、10和15 cm下土壤温度的均方根误差、平均绝对误差、均方误差和决定系数,并以4个评价指标进行验证。与自回归综合移动平均模型、LSTM和全连接网络相比,本文模型具有最优性能,尤其在未来6 h内对Fluehli站(10 cm土壤深度)土壤温度模型中改善最为显著;取得了最高的相对决定系数值0.9965,最低的均方根误差为0.3414,平均绝对误差为0.2310,均方误差为0.1165。因此,本文模型可以作为备选土壤温度估计的替代方法。

In order to accurately analyze the problem of soil temperature characteristics,this paper proposes a prediction model based on attention mechanism of multi-channel long and short-term memory network fused with ARIMA algorithm.The attention-based multi-channel long-and short-term memory network is used to extract important temporal features at different moments of the long and short term,and the ARIMA time series model is used to extract linear features to take advantage of predicting soil temperature more accurately.To validate the proposed model,four evaluation metrics,root mean square error,mean absolute error,mean square error and coefficient of determination,were tested at two weather stations in Switzerland(Laegern and Fluehli weather stations)for the next 6,12 and 24 hours,while soil depths on 5,10 and 15 cm soil temperatures.Compared with thse autoregressive integrated moving average model,the long and short-term memory network and the fully connected network,the proposed model has the optimal performance,especially the most significant improvement in the soil temperature model for Fluehli station(10 cm soil depth)during the next 6 hours;the highest relative coefficient of determination value of 0.9965,the lowest root mean square error of 0.3414,the average absolute error of 0.2310 and mean squared error of 0.1165.Therefore,the proposed model can be used as an alternative alternative method for soil temperature estimation.