Visibility forecast in Jiangsu province based on the GCN-GRU model

Abstract

Abstract Low visibility weather easily leads to traffic accidents, posing threats to human life and property. To accurately forecast visibility, we conduct an empirical study focusing on Jiangsu Province. Firstly, we collect the monitoring data from meteorological stations and environmental stations for 2017-2018. Secondly, we analyze the changes in visibility from both spatial and temporal perspectives. Next, the maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select factors affecting visibility, finding that humidity and $$PM_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:msub> <mml:mi>M</mml:mi> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> concentrations are the primary factors. Finally, we propose GCN-GRU (Graph Convolutional Network and Gated Recurrent Unit) model for short-term visibility forecasting, which employs GCN to capture the interactions between stations and uses GRU to learn the interactions between times. Experimental results indicate that GCN-GRU outperforms the standalone GRU model and three machine learning models regarding 6-hour visibility forecasting. Compared to the best competitor, GCN-GRU achieves an average increase of 3.32% in Correlation Coefficient (CORR), a decrease of 17.52% in Root Mean Square Error (RMSE), a reduction of 26.62% in Mean Absolute Percentage Error (MAPE), and a decline of 16.53% in Mean Absolute Error (MAE).

Topics & Concepts