Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran

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Ansari Ghojghar Mohammad; Pourgholam Amiji Masoud; BAZRAFSHAN JAVAD; LIAGHAT ABDOLMAJID; ARAGHINEJAD SHAHAB

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Journal: Iranian Journal of Soil and Water Research Year:2020 | Volume:51 | Issue:8 Page(s): 2051-2063

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Title

Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran

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Keywords

Adaptive Neural-Fuzzy Inference SystemQ2

Artificial IntelligenceQ1

DustQ1

ForecastingQ1

Radial Basis FunctionsQ1

Abstract

Different regions have different potentials in Dust release, and the increase in Dust storms indicates the dominance of the desert ecosystem in each region. Prediction of the occurrence of Dust storms in critical regions allow desion-makers to efficiently manage and to mitigate its probable damages to landscape. This study aims to predict the frequency of Dust storm days (FDSD) over two critical regions (west and southeast) in Iran on a seasonal scale. To this end, the hourly Dust data and World Meteorological Organization codes were gathered in six synoptic stations of Zabol and Zahedan (southeast Iran), Abadan, Ahvaz, Bostan, and Masjed Soleiman (west Iran) covering the statistical period of 25 years (1990-2014). After calculating the frequency of Dust storm days, using four Artificial Intelligence methods including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), and general regression neural network (GRNN), the frequency of Dust storm days for the next season were predicted. The results showed an increase in the accuracy of the predictions with increasing the number of Dust storm days in such a way that based on the results obtained from the MLP model, the correlation coefficient between the observed and predicted values of the frequency of Dust storm days for Masjed Soleiman and Zabol stations were 0. 8 and 0. 97, respectively; explaining that Zabol have the highest frequency among these stations. Also, according to the results of ANFIS, RBF, and GRNN models, the correlation coefficient calculated for prediction in Masjed Soleiman and Zabol stations varied from 0. 41 to 0. 95, 0. 35 to 0. 92 and 0. 22 to 0. 98, respectively. Overall, by comparing the results of the proposed models, ANFIS had the best performance which was followed by GRNN. The results of this study can be useful in managing the issues caused by Dust storms and in the combating plans to desertification in the study regions.

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APA: Copy

Ansari Ghojghar, Mohammad, Pourgholam Amiji, Masoud, BAZRAFSHAN, JAVAD, LIAGHAT, ABDOLMAJID, & ARAGHINEJAD, SHAHAB. (2020). Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran. IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, 51(8 ), 2051-2063. SID. https://sid.ir/paper/370051/en

Vancouver: Copy

Ansari Ghojghar Mohammad, Pourgholam Amiji Masoud, BAZRAFSHAN JAVAD, LIAGHAT ABDOLMAJID, ARAGHINEJAD SHAHAB. Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran. IRANIAN JOURNAL OF SOIL AND WATER RESEARCH[Internet]. 2020;51(8 ):2051-2063. Available from: https://sid.ir/paper/370051/en

IEEE: Copy

Mohammad Ansari Ghojghar, Masoud Pourgholam Amiji, JAVAD BAZRAFSHAN, ABDOLMAJID LIAGHAT, and SHAHAB ARAGHINEJAD, “Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran,” IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, vol. 51, no. 8 , pp. 2051–2063, 2020, [Online]. Available: https://sid.ir/paper/370051/en

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