首页|融合自注意力机制的双向LSTM时空插值模型

融合自注意力机制的双向LSTM时空插值模型

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时空插值可以捕获时空数据中的依赖关系,估计地理现象随时间的几何和属性数据变化.现有的时空插值方法大多未同时考虑数据的长期时间相关性以及全局空间信息,本文结合长短时记忆网络LSTM(Long Short Term Memory)与数据的空间特性构建了时空插值模型:①模型利用空间层剔除弱相关性的信息,提取相关性更强的空间信息输入LSTM网络;②由于传统人工神经网络ANN(Artificial Neural Network)模型无法考虑时间对插值的影响以及单向LSTM模型仅能考虑过去时刻对当前时刻的影响而不能利用未来时刻的信息,本文使用双向LSTM模型BiLSTM(Bi-directional LSTM)体现时间相关性;③为了有效提取全局空间特征并保留BiLSTM双向建模的优势,本文将自注意力机制引入BiLSTM中,构建了融合自注意力的双向LSTM插值模型SL-BiLSTM-SA(BiLSTM Model Fused with Spatial Layer-Self attention).在实验设计阶段,模型被应用于山东省PM2.5浓度数据集进行插值效果研究,并与其它模型进行性能比较.实验表明,SL-BiLSTM-SA模型有着更低的误差度量,相较时空普通克里金STOK(Spatio-Temporal Ordinary Kriging)和遗传算法优化的时空克里金GA-STK(Genetic Al-gorithm-optimized Spatio-Temporal Kriging)精度分别提高了39.83％、36.63％,且能较准确地预测高值和低值.本文融合空间信息,结合BiLSTM和Self-attention构建了时空插值模型,扩展了时空数据的插值手段,为时空数据分析提供了一定的理论和方法支撑.
A Bidirectional LSTM Spatiotemporal Interpolation Model with Self-attention Mechanism
Spatial-temporal data missingness and sparsity are prevalent phenomena,for which spatial-temporal interpolation serves as a critical methodology to address these issues. Spatial-temporal interpolation constitutes a significant research domain within the field of Geographical Information Science. This technique enables the capture of dependencies in spatial-temporal data and the estimation of the geometric and attribute variations of geographical phenomena over time. With the advancement of geospatial technologies,particularly Geographic Information Systems,contemporary spatial-temporal interpolation methods predominantly rely on statistical,machine learning,and deep learning approaches that account for both temporal and spatial dimensions. These methods aim to reveal the evolutionary processes and spatial-temporal distribution patterns inherent in the data. However,a majority of such techniques often overlook long-term dependencies and contextual spatial information when interpolating. This study proposes an innovative model that intertwines Long Short-Term Memory (LSTM) networks with spatial attributes to address these limitations effectively. The proposed model operates through several key stages:(1) It employs a dedicated spatial layer to systematically eliminate weakly correlated information,focusing on extracting and feeding more significantly correlated spatial data into the LSTM network. (2) Given that conventional Artificial Neural Network (ANN) models are unable to consider the impact of the temporal dimension on interpolation,and unidirectional LSTM models can only factor in past moments' influence without utilizing future moment information,this research adopts a Bidirectional LSTM (BiLSTM) architecture. The BiLSTM inherently captures both spatial and temporal dependencies,thereby overcoming previous limitations. (3) To further enhance its performance by efficiently extracting comprehensive global spatial features while maintaining the advantages of bidirectional modeling offered by BiLSTM,we integrate a self-attention mechanism into the BiLSTM framework. This results in a novel,fused Bidirectional LSTM Interpolation Model with Spatial Layer-Self Attention (SL-BiLSTM-SA). In the experimental phase,the SL-BiLSTM-SA model is rigorously applied to a PM2.5 concentration dataset from Shandong Province to conduct a meticulous investigation into its interpolation capabilities. Upon comparative analysis against other models,it is evident that the SL-BiLSTM-SA model outperforms with notably lower error metrics,demonstrating substantial improvements in accuracy—by 39.83％ and 36.63％ when compared to Spatio-Temporal Ordinary Kriging (STOK) and Genetic Algorithm-optimized Spatio-Temporal Kriging (GA-STK) methods,respectively. Moreover,our model exhibits commendable precision in forecasting high and low concentration levels. By seamlessly integrating spatial information and coupling the strengths of BiLSTM with self-attention mechanisms,this research not only extends the suite of interpolation methods for spatiotemporal data analysis but also furnishes robust theoretical underpinnings and methodological support to facilitate sophisticated spatiotemporal data analyses.

spatiotemporal interpolationspatiotemporal correlationspatial layerlong-term correlationbidirectional long-and short-term memory networkself-attention mechanismPM2.5

周啸宇、王海起、王琼、单宇飞、闫峰、李发东、刘峰、曹元昊、欧雅玟、李雪莹

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中国石油大学(华东) 海洋与空间信息学院,青岛 266580

南京生兴有害生物防治技术股份有限公司,南京 211100

时空插值 时空相关性 空间层 长期相关性 双向长短期记忆网络 自注意力机制 PM2.5

山东省自然科学基金面上项目

ZR2021MD068

2024

10.12082/dqxxkx.2024.230574

地球信息科学学报
中国科学院地理科学与资源研究所

地球信息科学学报

CSTPCD北大核心
影响因子：1.004
ISSN：1560-8999
年,卷(期)：2024.26(8)