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Deep shared representation learning for weather elements forecasting
- Source :
- Knowledge-Based Systems, 179, 120-128. Elsevier Science
- Publication Year :
- 2019
- Publisher :
- Elsevier Science, 2019.
-
Abstract
- The accuracy and reliability of weather forecasting are of importance for many economic, business and management activities. This paper introduces novel data-driven predictive models based on deep convolutional neural networks (CNN) architecture for temperature and wind speed prediction in weather data. In particular, the proposed deep learning framework employs different upgrading versions of the convolutional neural networks i.e. 1d-, 2d- and 3d-CNN. The introduced models exploit the spatio-temporal multivariate weather data for learning shared representations using historical data and forecasting weather elements for a number of user defined weather stations simultaneously in an end-to-end fashion. The embedded feature learning component of the models as well as coupling the learned features of different input layers have shown to have a significant impact on the prediction task. The proposed models show promising results compared to the classical neural networks architecture used for modeling nonlinear systems. Two experimental setups have been considered based on a dataset collected from the Weather Underground website at six stations located in Netherlands and Belgium as well as a larger dataset with higher temporal resolution from the National Climatic Data Center (NCDC) at five stations located in Denmark. First, we focus on simultaneously predicting the temperature of two main stations of Amsterdam and Brussels for 1-10 days ahead. The second experiment concerns wind speed prediction at three weather stations located in Denmark for 6 and 12 h ahead. The obtained numerical results show that learning new shared representations of the weather data by means of convolutional operations improves the prediction performance. (C) 2019 Elsevier B.V. All rights reserved.
- Subjects :
- Information Systems and Management
Computer science
Reliability (computer networking)
Weather forecasting
02 engineering and technology
computer.software_genre
Convolutional neural network
Wind speed
Representation learning
Management Information Systems
Artificial Intelligence
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Artificial neural network
business.industry
Deep learning
Dimensionality reduction
NETWORKS
020201 artificial intelligence & image processing
Convolutional neural networks
Data mining
Artificial intelligence
business
Feature learning
computer
Software
Subjects
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 179
- Database :
- OpenAIRE
- Journal :
- Knowledge-Based Systems
- Accession number :
- edsair.doi.dedup.....4a143b0e8d950f08b0881c14d5cd3a1f