Dust Storm Prediction Using ANNs Technique (A Case Study: Zabol City)
Dust storms are one of the most costly and destructive
events in many desert regions. They can cause massive damages both
in natural environments and human lives. This paper is aimed at
presenting a preliminary study on dust storms, as a major natural
hazard in arid and semi-arid regions. As a case study, dust storm
events occurred in Zabol city located in Sistan Region of Iran was
analyzed to diagnose and predict dust storms. The identification and
prediction of dust storm events could have significant impacts on
damages reduction. Present models for this purpose are complicated
and not appropriate for many areas with poor-data environments. The
present study explores Gamma test for identifying inputs of ANNs
model, for dust storm prediction. Results indicate that more attempts
must be carried out concerning dust storms identification and
segregate between various dust storm types.
[1] Barnum, B. H., Winstead, N. S., Wesely, J., Hakola, A., Colarco, P. R.,
Toon, O. B., Ginoux, P., Brooks, G., Hasselbarth, L., and Toth, B., 2004,
Forecasting dust storms using the CARMA-dust model and MM5
weather data. Journal of Environmental Modelling & Software 19: 129-
140.
[2] COMET (Cooperative Program for Operational Meteorology), 2003,
Mesoscale Primer. Forecasting Dust Storms. University Corporation for
Atmospheric Research. Sections 3.1.1, 3.1.2, 3.2.2, 3.2.5, 3.3.3, 5.2.1,
6.3.
[3] C. Todd, M., Washington, R., Vanderlei Martins, J., Dubovik, O.,
Lizcano, G., M-Bainayel, S., and Engelstaedter, S., 2007, Mineral dust
emission from the Bodélé Depression, northern Chad, during BoDEx
2005. Journal of geophysical Research 112: D06207,
doi:10.1029/2006JD007170.
[4] Dhondia, J., and Diermanse, F., 2006. Integrated Water Resources
Management for the Sistan Closed Inland Delta, Iran. Annex F - FFS
Manual. Delft hydraulics.
[5] Donaldson, R., Dyer, R., and Krauss, M., 1975. An objective evaluator
of techniques for predicting severe weather events, in Preprints, Ninth
Conference on Severe Local Storms (Norman, OK), American
Meteorological Society, 321-326.
[6] Dunn, G., and S. Everitt, B., 2001, Applied Multivariate Data Analysis.
Published by Oxford University Press US.
[7] Ekstrom, M., H. MC Tanish, G., and Cappell, A., 2004, Australian Dust
Storms: Temporal trends and relationships with synoptic pressure
distributions (1960-99). International Journal of climatology 24: 1581-
1599.
[8] Hu, X., Q., Lu, N. M., Niu, T., and Zhang, P., 2007, dust storm from FY-
2C geostationary meteorological satellite and its application to real time
forecast in Asia. Atmospheric Chemistry and Physics Discussions 7:
8395-8421.
[9] Huang, M., Peng, G., Zhang, J., and Zhang, S., 2006, Application of
artificial neural networks to the prediction of dust storms in Northwest
China. Journal of Global and Planetary Change, 52: 216-224.
[10] Lu, Z., Zhang, Q., and Zhao, Z., 2006, SVM in the sand-dust storm
forecasting. Proceedings of the Fifth International Conference on
Machine Learning and Cybernetics, Dalian, 13-16 August 2006.
[11] Middleton, N. J., 1986a. Geography of dust storms in South-West Asia.
Journal of Climatology 6,183-196.
[12] Natsagdorj, L., Jugder, D., and Chung, Y. S., 2002, Analysis of Dust
Storms Observed in Mongolia. Journal of the Korean meteorological
society: 38, 209-223.
[13] Novlan, D. J., Hardiman, M., and Gill, T. E., 2007, A synoptic
climatology of blowing dust events in El Paso, Texas from 1932-2005.
Preprints, 16th Conference on Applied Climatology, American
Meteorological Society, J3.12, 13 pp.
[14] Okin, G. S., and Reheis, M. C., 2002, An ENSO predictor of dust
emission in the southwestern United States. Geophys. Res. Lett., 29,
1332, 10.1029/2001GL014494.
[15] Orlovsky, L., Orlovsky, N., and Durdyev, A., 2005. Dust storms in
Turkmenistan. Journal of Arid Environments 60: 83-97.
[16] Qiang, M., Chen, F., Zhou, A., Xiao, S., Zhang, J., and Wang, Z., 2007,
Impacts of wind velocity on sand and dust deposition during dust storm
as inferred from a series of observations in the northeastern Qinghai-
Tibetan Plateau, China. Powder Technology 175 : 82-89.
[17] Romanov, N. N., 1961. Dust Storms in Central Asia (Pyl-nye buri
Srednei Asii). Samarkand University, Tashkent, 198pp. (in Russian).
[18] Singh , S., 2005, Implementation of the Gamma test in MATLAB using
a fast near neighbor search algorithm in C++. A dissertation submitted in
partial fulfillment of the requirement for the degree of MSc Department
of Computer Science, Cardiff University.
[19] Soudant, D., Beliaef, B., and Thomas, G., 1997, Explaining Dinophysis
cf. acuminata abundance in Antifer (Normandy, France) using dynamic
linear regression. journal Marine ecology progress seris, 156: 67-74.
[20] Stefánsson, A., Koncar, N., and J. Jones, A., 1997, A note on the Gamma
test, Neural Computing & Applications 5(3):131-133.
[21] The winGamma User Guide. Copyright: University of Wales, Cardiff,
1998-2001.
[22] Youlin, Y., Squires, V., Lu Q., (Eds.), 2001, Global Alarm: Dust and
Sand Storms from the World-s Drylands. UNCCD, Bangkok, pp. 169-
201.
[1] Barnum, B. H., Winstead, N. S., Wesely, J., Hakola, A., Colarco, P. R.,
Toon, O. B., Ginoux, P., Brooks, G., Hasselbarth, L., and Toth, B., 2004,
Forecasting dust storms using the CARMA-dust model and MM5
weather data. Journal of Environmental Modelling & Software 19: 129-
140.
[2] COMET (Cooperative Program for Operational Meteorology), 2003,
Mesoscale Primer. Forecasting Dust Storms. University Corporation for
Atmospheric Research. Sections 3.1.1, 3.1.2, 3.2.2, 3.2.5, 3.3.3, 5.2.1,
6.3.
[3] C. Todd, M., Washington, R., Vanderlei Martins, J., Dubovik, O.,
Lizcano, G., M-Bainayel, S., and Engelstaedter, S., 2007, Mineral dust
emission from the Bodélé Depression, northern Chad, during BoDEx
2005. Journal of geophysical Research 112: D06207,
doi:10.1029/2006JD007170.
[4] Dhondia, J., and Diermanse, F., 2006. Integrated Water Resources
Management for the Sistan Closed Inland Delta, Iran. Annex F - FFS
Manual. Delft hydraulics.
[5] Donaldson, R., Dyer, R., and Krauss, M., 1975. An objective evaluator
of techniques for predicting severe weather events, in Preprints, Ninth
Conference on Severe Local Storms (Norman, OK), American
Meteorological Society, 321-326.
[6] Dunn, G., and S. Everitt, B., 2001, Applied Multivariate Data Analysis.
Published by Oxford University Press US.
[7] Ekstrom, M., H. MC Tanish, G., and Cappell, A., 2004, Australian Dust
Storms: Temporal trends and relationships with synoptic pressure
distributions (1960-99). International Journal of climatology 24: 1581-
1599.
[8] Hu, X., Q., Lu, N. M., Niu, T., and Zhang, P., 2007, dust storm from FY-
2C geostationary meteorological satellite and its application to real time
forecast in Asia. Atmospheric Chemistry and Physics Discussions 7:
8395-8421.
[9] Huang, M., Peng, G., Zhang, J., and Zhang, S., 2006, Application of
artificial neural networks to the prediction of dust storms in Northwest
China. Journal of Global and Planetary Change, 52: 216-224.
[10] Lu, Z., Zhang, Q., and Zhao, Z., 2006, SVM in the sand-dust storm
forecasting. Proceedings of the Fifth International Conference on
Machine Learning and Cybernetics, Dalian, 13-16 August 2006.
[11] Middleton, N. J., 1986a. Geography of dust storms in South-West Asia.
Journal of Climatology 6,183-196.
[12] Natsagdorj, L., Jugder, D., and Chung, Y. S., 2002, Analysis of Dust
Storms Observed in Mongolia. Journal of the Korean meteorological
society: 38, 209-223.
[13] Novlan, D. J., Hardiman, M., and Gill, T. E., 2007, A synoptic
climatology of blowing dust events in El Paso, Texas from 1932-2005.
Preprints, 16th Conference on Applied Climatology, American
Meteorological Society, J3.12, 13 pp.
[14] Okin, G. S., and Reheis, M. C., 2002, An ENSO predictor of dust
emission in the southwestern United States. Geophys. Res. Lett., 29,
1332, 10.1029/2001GL014494.
[15] Orlovsky, L., Orlovsky, N., and Durdyev, A., 2005. Dust storms in
Turkmenistan. Journal of Arid Environments 60: 83-97.
[16] Qiang, M., Chen, F., Zhou, A., Xiao, S., Zhang, J., and Wang, Z., 2007,
Impacts of wind velocity on sand and dust deposition during dust storm
as inferred from a series of observations in the northeastern Qinghai-
Tibetan Plateau, China. Powder Technology 175 : 82-89.
[17] Romanov, N. N., 1961. Dust Storms in Central Asia (Pyl-nye buri
Srednei Asii). Samarkand University, Tashkent, 198pp. (in Russian).
[18] Singh , S., 2005, Implementation of the Gamma test in MATLAB using
a fast near neighbor search algorithm in C++. A dissertation submitted in
partial fulfillment of the requirement for the degree of MSc Department
of Computer Science, Cardiff University.
[19] Soudant, D., Beliaef, B., and Thomas, G., 1997, Explaining Dinophysis
cf. acuminata abundance in Antifer (Normandy, France) using dynamic
linear regression. journal Marine ecology progress seris, 156: 67-74.
[20] Stefánsson, A., Koncar, N., and J. Jones, A., 1997, A note on the Gamma
test, Neural Computing & Applications 5(3):131-133.
[21] The winGamma User Guide. Copyright: University of Wales, Cardiff,
1998-2001.
[22] Youlin, Y., Squires, V., Lu Q., (Eds.), 2001, Global Alarm: Dust and
Sand Storms from the World-s Drylands. UNCCD, Bangkok, pp. 169-
201.
@article{"International Journal of Architectural, Civil and Construction Sciences:54674", author = "Jamalizadeh and M.R. and Moghaddamnia and A. and Piri and J. and Arbabi and V. and Homayounifar and M. and Shahryari and A.", title = "Dust Storm Prediction Using ANNs Technique (A Case Study: Zabol City)", abstract = "Dust storms are one of the most costly and destructive
events in many desert regions. They can cause massive damages both
in natural environments and human lives. This paper is aimed at
presenting a preliminary study on dust storms, as a major natural
hazard in arid and semi-arid regions. As a case study, dust storm
events occurred in Zabol city located in Sistan Region of Iran was
analyzed to diagnose and predict dust storms. The identification and
prediction of dust storm events could have significant impacts on
damages reduction. Present models for this purpose are complicated
and not appropriate for many areas with poor-data environments. The
present study explores Gamma test for identifying inputs of ANNs
model, for dust storm prediction. Results indicate that more attempts
must be carried out concerning dust storms identification and
segregate between various dust storm types.", keywords = "Dust Storm, Gamma Test, Prediction, ANNs, Zabol.", volume = "2", number = "7", pages = "153-9", }
