LS-NTP: Unifying long- and short-range spatial correlations for near-surface temperature prediction. (November 2022)

• Record Type: Journal Article
• Authors: Xu, Guangning; Li, Xutao; Feng, Shanshan; Ye, Yunming; Tu, Zhihua; Lin, Kenghong; Huang, Zhichao
• Is Part Of: Neural networks. Volume 155(2022)
• Page Start: 242
• Page End: 257
• Publication Date: 2022-11
• Subjects: temperature prediction -- Near-surface temperature -- Long-range -- Short-range -- Spatial–temporal -- CNN -- GCN; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.07.022
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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 . Highlights: 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.