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Applied Sciences
Special Issues
Artificial Neural Networks, Theory, Methods and Applications
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Artificial Neural Networks, Theory, Methods and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 2712

Special Issue Editor

Prof. Pegalajar Cuellar Manuel

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
Interests: machine learning; artificial neural networks; multiobjective optimization; swarm intelligence

Special Issue Information

Dear Colleagues,

An artificial neural network (ANN) is the piece of a computing system designed to simulate the way in which the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. It is a subset of machine learning and is at the core of deep learning algorithms. The artificial neural network has seen a sharp increase in interest in recent years and is successfully applied across an extraordinary range of problem domains, such as handwriting recognition, image compression, the traveling salesman problem, and stock exchange prediction. The computing world has a great deal to gain from the neural network.

We invite researchers and investigators to contribute their original research or review articles to this Special Issue, the aim of which is to bring together academics and industrial practitioners to exchange and discuss the latest theory, methods, and applications of the ANN. Papers addressing, but not limited to, the following topics will be considered for publication.

Prof. Pegalajar Cuellar Manuel
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial neural networks
  • deep learning
  • machine learning
  • pattern recognition

Published Papers (1 paper)

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Research

15 pages, 450 KiB  
Article
Post-Processing Air Temperature Weather Forecast Using Artificial Neural Networks with Measurements from Meteorological Stations
by Gustavo Araujo and Fabio A. A. Andrade
Appl. Sci. 2022, 12(14), 7131; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147131 - 15 Jul 2022
Cited by 7 | Viewed by 1391
Abstract
Human beings attempt to accurately predict the weather based on their knowledge of climate. The Norwegian Meteorological Institute is responsible for climate-related matters in Norway, and among its contributions is the numerical weather forecast, which is presented in a 2.5 km grid. To [...] Read more.
Human beings attempt to accurately predict the weather based on their knowledge of climate. The Norwegian Meteorological Institute is responsible for climate-related matters in Norway, and among its contributions is the numerical weather forecast, which is presented in a 2.5 km grid. To conduct a post-processing process that improves the resolution of the forecast and reduces its error, the Institute has developed the GRIDPP tool, which reduces the resolution to 1 km and introduces a correction based on altitude and meteorological station measurements. The present work aims to improve the current post-processing approach of the air temperature parameter by employing neural networks, using meteorological station measurements. Two neural network architectures are developed and tested: a multilayer perceptron and a convolutional neural network. Both architectures are able to achieve a smaller error than the original product. These results open doors for the Institute to plan for the practical implementation of this solution on its product for specific scenarios where the traditional numerical methods historically produce large errors. Among the test samples where the GRIDPP error is higher than 3 K, the proposed solution achieves a smaller error in 74.8% of these samples. Full article
(This article belongs to the Special Issue Artificial Neural Networks, Theory, Methods and Applications)
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