Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network

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Author(s):
Angshuman Ray, Sourav Mukhopadhyay, Bimal Datta, Srimanta Pal
Published Date:
January 05, 2013
Issue:
Volume 3, Issue 1
Page(s):
11 - 18
DOI:
10.7815/ijorcs.31.2013.056
Views:
4519
Downloads:
164

Keywords:
artificial neural networks, backpropagation, data clustering, multi-layer perceptron, pressure
Citation:
Angshuman Ray, Sourav Mukhopadhyay, Bimal Datta, Srimanta Pal, "Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network". International Journal of Research in Computer Science, 3 (1): pp. 11-18, January 2013. doi:10.7815/ijorcs.31.2013.056 Other Formats

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

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