Work

Physical Attention-Gated Spatial-Temporal Predictive Network for Weather Forecasting

Year: 2023

Type: article

Abstract: Spatial-temporal sequence prediction is one of the hottest topics in the field of deep learning due to its wide range of potential applications in video-like data processing, specifically weather forecasting. Since most spatial-temporal observations evolve under physical laws, we adopt an attentional gating scheme to leverage the dynamic patterns captured by tailored convolution structures and propose a novel neural network, PastNet, to achieve accurate predictions. By highlighting useful parts of the whole feature map, the gating units help increase the efficiency of the architecture. Extensive experiments conducted on synthetic and real-world datasets reveal that PastNet bears the ability to accomplish this task with better performance than baseline methods.

Source: Mathematics

DOI: 10.3390/math11061330

Authors: Xueliang Zhao, Qilong Sun, Xiaoguang Lin

Institutions: Chinese Academy of Sciences, Chongqing Institute of Green and Intelligent Technology, University of Chinese Academy of Sciences

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,336

Funder: Department of Science and Technology of Inner Mongolia