Application of artificial neural networks to the prediction of dust storms in Northwest China [An article from: Global and Planetary Change]

This digital document is a journal article from Global and Planetary Change, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Artificial neural networks (ANN) are non-linear mapping structures analogous to the functioning of the human brain. In this study, we take the ANN approach to model and predict the occurrence of dust storms in Northwest China, by using a combination of daily mean meteorological measurements and dust storm occurrence. The performance of the ANN model in simulating dust storm occurrences is compared with a stepwise regression model. The correlation coefficients between the observed and the estimated dust storm occurrences obtained from the neural network procedure are found to be significantly higher than those obtained from the regression model with the same input data. The prediction tests show that the ANN models used in this study have the potential of forecasting dust storm occurrence in Northwest China by using conventional meteorological variables.