IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Analysis and Prediction of Meteorological Data Based on Edge Computing and Neural Network

Analysis and Prediction of Meteorological Data Based on Edge Computing and Neural Network
View Sample PDF
Author(s): Jianxin Wang (Anyang National Climatological Observatory, China) and Geng Li (Henan Polytechnic University, China)
Copyright: 2022
Volume: 13
Issue: 2
Pages: 10
Source title: International Journal of Distributed Systems and Technologies (IJDST)
Editor(s)-in-Chief: Nik Bessis (Edge Hill University, UK)
DOI: 10.4018/IJDST.291081

Purchase

View Analysis and Prediction of Meteorological Data Based on Edge Computing and Neural Network on the publisher's website for pricing and purchasing information.

Abstract

In this work, aiming at the problem of missing element values in real-time meteorological data, we propose a radial basis function (RBF) neural network model based on rough set to optimize the analysis and prediction of meteorological data. In this model, the relative humidity of a single station is taken as an example, and the meteorological influencing factors are reduced by rough set theory. The key factors are used as the input of RBF neural network to interpolate the missing data. The experimental results show that the interpolation effect of the model is significantly higher than that of the linear interpolation method, which provides an effective processing method for the lack of real-time meteorological data, and improves the analysis and prediction effect of meteorological data.

Related Content

. © 2022.
Liguo Wang, Haibin Yang. © 2022. 14 pages.
Qi Zhang. © 2022. 8 pages.
Chenchen Yao, Chuangang Zhao. © 2022. 11 pages.
Jianxin Wang, Geng Li. © 2022. 10 pages.
Jing Fu, Zipeng Han. © 2022. 10 pages.
Victor Chang, Keerthi Kandadai, Qianwen Ariel Xu, Steven Guan. © 2022. 22 pages.
Body Bottom