An Efficient Weather Forecasting System using a Hybrid Neural Network SOFM–MLP
Main Article Content
Abstract
Weather prediction is a challenging task for researchers and has drawn a lot of research interest in the recent years. Literature studies
have shown that machine learning techniques achieved better performance than traditional statistical methods. Presently multilayer perceptron
networks (MLPs) are used for prediction of the maximum and the minimum temperatures based on past observations on various atmospheric
parameters. To capture the seasonality of atmospheric data, with a view to improving the prediction accuracy, a novel weather forecasting
system is presented in this paper. The proposed system is based on a neural architecture that combines a selforganizing feature map (SOFM) and
MLPs to realize a hybrid network named SOFM–MLP. It is also demonstrated that the use of appropriate features such as temperature gradient
can not only reduce the number of features drastically, but also can improve the prediction accuracy. These observations motivated us to use a
feature selection MLP (FSMLP) instead of MLP, which can select good features online while learning the prediction task. FSMLP is used as a
preprocessor to select good features. The combined use of FSMLP and SOFM–MLP provides better result in a network system that uses only
very few inputs but can produce good prediction. The proposed system is experimented using the real time data observations and from which it
is found that the proposed system predict the temperature with minimum error.
Â
Keywords: Atmospheric science, back propagation, feature selection, neural networks, self-organizing feature map (SOFM), temperature
forecasting
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.