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Author Yixue, Meng
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher EDP Sciences
File Format PDF
Date Created 2018-07-27
Copyright Year ©2018
Language English ♦ French
Subject Domain (in LCC) TA1-2040
Subject Keyword Engineering (General) ♦ Technology ♦ Civil engineering (General)
Abstract Intelligent detection of surface temperature 1, describing macro characteristics of microcosmic combination, promotes the cross fusion of geoscience, thermodynamics, climatology, geological science, and so on. However, there are still two notable problems to be solved. One is the model lacks characterization capability, and the other is that the precision of surface temperature’s monitoring and prediction is low. To solve these problems, we propose an algorithm to predict surface temperature characteristics of multiscale fusion based on convolution neural network. Firstly, after researching the multiscale disturbance characteristics of surface temperature, we draw a conclusion based on analyzing time change, spatial change, casual change. To improve the parameter correlations among surface temperature characteristics, a neural network about compensating and optimizing analysis of surface temperature characteristics is proposed on the fundamental of multivariate surface temperature characterization models. By designing cluster input layer, dynamic hidden layer and visual output layer of neural network, the precise of predict data has been improved by 53.3% on average, and 76.0% on variance compared with remote sensing data. What’s more, the data consumption of this model has promoted by 17.2% in contrast to grey theory on predictive complexity and precision, and 10.8% compared with BP neural network.
ISSN 2261236X
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2018-01-01
e-ISSN 2261236X
Journal MATEC Web of Conferences
Volume Number 173
Starting Page 03011


Source: Directory of Open Access Journals (DOAJ)