Validation of Synoptic Station Data Using Ensemble Classification on Central Iran

Today, the use of data recorded in synoptic stations of the country is one of the most significant sources of applied research for researchers. Data recorded automatically or manually at synoptic, climatological, and other stations are analyzed for statistical analysis. In this research, the data recorded in the synoptic stations of Iran, which are used to determine the days of dust, were analyzed using the science of monitoring and data analysis using ensemble classification. In this study, data from 36 synoptic stations, were used. These stations are in Isfahan, Kerman, Yazd, Sistan and Baluchestan, Semnan, Markazi, Khorasan Razavi, Hamedan, Qom, and South Khorasan. The parameters of daily average temperature, daily rainfall, station height, geographical location of the station, maximum wind speed, maximum wind speed, and dew point were used for the classification. The results showed that the most important factor among these parameters for dust is the maximum wind speed, which was identified as the most significant factor in all classification methods. Also, three classifiers, KNN, SVM with RBF kernel, and MLP neural network, were selected as members of the ensemble, which accurately detects 90.7 percent of the source of dust production (from the inside and outside the dust).