Author(s): Angshuman Ray | Sourav Mukhopadhyay | Bimal Datta
Journal: International Journal of Research In Computer Science
ISSN 2249-8257
Volume: 3;
Issue: 1;
Start page: 11;
Date: 2013;
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Keywords: artificial neural networks | backpropagation | data clustering | multi-layer perceptron | pressure
ABSTRACT
Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP) to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster
Journal: International Journal of Research In Computer Science
ISSN 2249-8257
Volume: 3;
Issue: 1;
Start page: 11;
Date: 2013;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: artificial neural networks | backpropagation | data clustering | multi-layer perceptron | pressure
ABSTRACT
Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP) to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster
