Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran

Shahram Kaboodvandpour (), Jamil Amanollahi (), Samira Qhavami () and Bakhtiyar Mohammadi ()

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 78, issue 2, 879-893

Abstract: Dust storms in the Sanandaj area in the western region of Iran, mainly during spring and summer, have become an environmental crisis. Prediction of dust storm occurrences helps the residents to their detrimental effects. However, no study has been conducted to determine the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in predicting dust storm occurrences. For that purpose, the prediction accuracy of ANFIS model was compared with that of two conventional models used for dust storm prediction: the Artificial Neural Networks (ANN), and Multiple Regression (MLR) models. Daily mean meteorological variables from Damascus (Syria) and PM 10 concentration, measured at a ground station in Sanandaj, Iran, from 2009 to 2012, were selected as independent and dependent variables, respectively. After data normalization between zero and one, the data from 2009 to 2011 were used for the simulation, while the data of 2012 were utilized for testing the models. The performance of the ANFIS model in simulating dust storm occurrences was higher compared with those of MLR and ANN. In the simulation results, among the three models, the highest Pearson correlation coefficient between the observed and the estimated dust storm occurrences was obtained for the ANFIS model. The prediction tests showed that the accuracy of the ANFIS model was higher compared with ANN and MLR. From the results of this study, it can be concluded that the ANFIS model has the potential to forecast dust storm occurrences in western Iran by using meteorological variables of the dust storm creation zone in the Syrian deserts. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Simulate; Test; Meteorological variables; PM 10; Ground station; Desert (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11069-015-1748-0

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