Check the Phenomenon of Dust in Khuzestan Province Using Decision Models CHAID and CRT

Document Type : Original Article

Abstract

Check the Phenomenon of Dust in Khuzestan Province Using
Decision Models CHAID and CRT
 
 
 
 
 
1Karami, M.; 2*Sarvestan, R.; 3Moradimajd, N.
 
1 Department of Geography, Hakim Sabzevari University, Sabzevar, Iran
2 PhD Student of Urban Climatology, Hakim Sabzevar University, Sabzevar, Iran
3 PhD Student of Agricultural Meteorology, Geography and Environmental Sciences,
Hakim Sabzevari University, Sabzevar, Iran
 
(Received: 2016/11/28; Accepted: 2018/10/02)
 
 
 
Abstract
Aim of this study was to investigate the phenomenon of dust in the Khuzestan province and the correlation between these phenomena with elements of precipitation, speed wind and temperature is between 2010-1990. Statistical method library and using monthly data, eight selected stations (Masjed Soleiman, Ahvaz, Ramhormoz, Behbahan, Dezful, Aghajari, Abadan and Omidiyeh) of Khuzestan province using the softwares Minitab 17, spss 19 and Microsoft Excel 2013. For this purpose, using test homogeneity was examined data. From decision models was used to test, including automatic discovery classification method CHAID and regression trees CRT and test multiple interaction linear regression. Test Anderson-Darling and Kolmogorov-Smirnov showed that dust phenomenon data in Khuzestan province is normal. Using two methods CHAID and CRT was determined that wind is the best predictor for classifying dust in Khuzestan. Wind floors more than 2,100 in the CRT method and floor winds more than 6,500 in the CHAID method as a predictor of significant effect on the classification dust synoptic stations. Correlation between temperatur with dust is 0.425 percent, dust with wind speed 0.452 percent and between dust with rainfall -0.311 percent. With increase precipitation in the Khuzestan province reduced the number of days of dust but the temperature and wind speed are correlated to some extent with dust province and variables such as wind speed, rainfall and temperature have significant impact in the dust Khuzestan province.
 
 
 
 
 
 
 
Keywords: Dust; Rainfall; Wind speed; Temperature; CHAID; CRT; Khuzestan.
 
 
 
 
 
 
 
 

*Corresponding author:                                                                                 Email: r.sarvestan@gmail.com    

Keywords

Main Subjects

References

Aliabadi, K.; Asadi Zangene, M. A. & Dadashi Rodbari, A. 2016. Assessment and monitoring of the strom using remote sensing methods. Quarterly journal of emdad and nejat 1(7):1-20. (In Persian)
Asakereh, H. 2010. ARIMA modeling for the average annual temperature of the city of Tabriz, Journal of geography, 756: 15622-15601.
Bahrami, H. A.; Jalali, M.; Darvishi Bolorani, A. & Azizi, R. 2014. Modeling location dust stroms occur in Khouzestan province. Remote sensing and GIS Pp 95- 114. (In Persian)
Jamalizadeh, M. R.; Moghaddamnia, A.; Piri, J.; Arbabi, V.; Homayounifar, M. & Shahryari, A. 2008. Dust storm prediction using ANN technique (A case study: Zabol city). World academy of science: engineering and technology, 45: 529-537.
Keikhosravi, A. H. 2012. Statistical analysis synoptic dust storm Khorasan razavi province in the 2005-1993 period, Geographical Research, 65: 17-33.
Kurosaki, Y. & Mikami, M. 2003. Recent frequent dust events and their relation to surface wind in east Asia. geophy, 30: 17-36.
Maghami Moghim, Gh. R. 2016. Predict the frequency of dayswith the dust phenomenonin Bojnourd using the time series model AMIRA. First international congress on the applicationof newsciences in geographical studies of Iran. Pp 165- 173. (In Persian)
Malo, A. R. & Nicholson, S. E. 1990. A study of the rainfall and vegetation dynamics in the African sahel using the normalized difference vegetation index. Arid environ, 19: 1–24.
Nickling, W. G. & Brazel, A. J. 1984. Temporal and spatial characteristics of Arizona dust storms (1965–1980). Climatol, 4: 645–660.
Rashki, A.; Kaskaoutis, D. G.; Rautenbach, C. J.; Eriksson, P. G.; Qiang, M. & Gupta, P. 2012. Dust storms and their horizontal dust loading in the Sistan region. Iran. Aeolian research, 5: 51–62.
Salname Amari Khuzestan. 2013. Organization of management and budget planning. (In Persian)
Sun, J.H.; Zhao, L.N. & Zhao, S.X. 2003. An integrated modeling system of dust storm suitable to north China and applications. Clim environ, 8: 125–142.
Westwell, I. 1999. Fact finder guide weather. PRC publishing, 57-58 p.
Yosefi, M. & Kashi Zenouri, L. 2016. Determine the most stuitable method for combining artificial neural network input in order to determine the wind factors on the perdiction of dust strom phenomena ( case study: Yazd). Quarterly journal of research pasture and desert of Iran. 22(2): 240-250. (In Persian)
www.amar.org.ir.
Environmental Researches
Volume 9, Issue 18
June 2019
Pages 129-142

Files

Share

How to cite

Statistics