LS-NTP: Unifying long- and short-range spatial correlations for near-surface temperature prediction

• Published in: Neural networks 2022-11, Vol.155, p.242-257
• Main Authors: Xu, Guangning, Li, Xutao, Feng, Shanshan, Ye, Yunming, Tu, Zhihua, Lin, Kenghong, Huang, Zhichao
• Format: Article
• Language: English
• Subjects: CNN; GCN; Long-range; Near-surface temperature; Short-range; Spatial–temporal; temperature prediction
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2022.07.022

The near-surface temperature prediction (NTP) is an important spatial–temporal forecast problem, which can be used to prevent temperature crises. Most of the previous approaches fail to explicitly model the long- and short-range spatial correlations simultaneously, which is critical to making an accurate temperature prediction. In this study, both long- and short-range spatial correlations are captured to fill this gap by a novel convolution operator named Long- and Short-range Convolution (LS-Conv). The proposed LS-Conv operator includes three key components, namely, Node-based Spatial Attention (NSA), Long-range Adaptive Graph Constructor (LAGC), and Long- and Short-range Integrator (LSI). To capture long-range spatial correlations, NSA and LAGC are proposed to evaluate node importance aiming at auto-constructing long-range spatial correlations, which is named as Long-range aware Graph Convolution Network (LR-GCN). After that, the Short-range aware Convolution Neural Network (SR-CNN) accounts for the short-range spatial correlations. Finally, LSI is proposed to capture both long- and short-range spatial correlations by intra-unifying LR-GCN and SR-CNN. Upon the proposed LS-Conv operator, a new model called Long- and Short-range for NPT (LS-NTP) is developed. Extensive experiments are conducted on two real-world datasets and the results demonstrate that the proposed method outperforms state-of-the-art techniques. The source code is available on GitHub:https://github.com/xuguangning1218/LS_NTP. •We jointly incorporate long- and short-range spatial correlations in NTP prediction.•Theoretical findings proved that the proposed LS-Conv is a general version of CNN.•We develop a new spatial–temporal model named LS-NTP for the NTP task.•Extensive experiments are conducted on two real-world datasets.