Surface and high-altitude combined rainfall forecasting using convolutional neural network

• Published in: Peer-to-peer networking and applications Vol. 14; no. 3; pp. 1765 - 1777
• Main Authors: Zhang, Pengcheng, Cao, Wennan, Li, Wenrui
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Accuracy; Altitude; Artificial neural networks; Cluster analysis; Clustering; Communications Engineering; Computer Communication Networks; Economic forecasting; Engineering; Forecasting; High altitude; Information Systems and Communication Service; Machine learning; Meteorological data; Meteorology; Networks; Neural networks; Principal components analysis; Rainfall; Signal,Image and Speech Processing; Special Issue on P2P Computing for Deep Learning; Vector quantization; Weather stations; Convolutional neural network; Rainfall forecasting; Machine learning

Rainfall forecasting can guide human production and life. However, the existing methods usually have a poor prediction accuracy in short-term rainfall forecasting. Machine learning methods ignore the influence of the geographical characteristics of the rainfall area. The regional characteristics of...