Analysis and prediction of Dust phenomenon in the southwest of Iran

Sobhani, Behroz; Safarian Zengir, Vahid

doi:10.22111/jneh.2019.28148.1481

Analysis and prediction of Dust phenomenon in the southwest of Iran

Document Type : Research Article

Authors

¹ Professor, Dept. of physical Geography, Climatology, University of Mohaghegh Ardabili, Ardabil, Iran

² 2- Ph.D. student, Dept. of physical geography, Climatology, University of Mohaghegh Ardabili, Ardabil, Iran

10.22111/jneh.2019.28148.1481

Abstract

Dust is a phenomenon that has many destructive environmental impacts in different parts of human life, including: agriculture, economics, health and Etc. The country of Iran, especially its western and southwestern regions, is suffering a lot of damage due to its presence in the area affected by the dust phenomenon every year. So pay attention to this issue and reducing the resulting damage, It is a priority. The purpose of this Research is to investigate and predict the dust phenomenon in southwest of Iran. For this Research, 27-year-old dust data were used at 14 synoptic stations in southwest of Iran during the period (1990-2017). In this Research, dust data was first normalized in 14 stations Then, by using the hybrid-panel data model, the ANFIS-compatible neural network in MATLAB software was falsified and predicted. and finally, to prioritize more stations, Dust was exposed to TOPSIS and SAW multivariate decision making models. The findings of the study showed that the reliability of the lira faction models (neural network of the hybrid panel compared to the ANFIS comparative neural network) was higher. Based on prediction models, the maximum probability of occurrence, the maximum dust in the next 23 years in the studied area at two stations, Sarpol Zahab and Abadan are respectively (120.709, 128.917). According to the SAW model, the probability of occurrence of dust in the next 23 years is estimated at Abadan station with 0.99% and Based on the TOPSIS model, Islamabad Gharb station with a value of 0.97%. According to the results of this study, in order to reduce the damage caused by the dust phenomenon in the study area, in addition to domestic measures, such as inter-organizational cooperation, it should be addressed by concluding an international agreement with the neighboring countries.

Keywords

References

References (in Persian

Ahmadzadeh, G; Majid, M and Kourosh, M., (2010), Comparison of Artificial Intelligence Systems (ANN and ANFIS) in Estimating the Rate of Evapotranspiration of Reference Plants in Iran's High Drylands. Journal of Soil and Water, 4(5), pp.679-689. [In Persian]

Ammarloo, J., Javid, H., Shagarian, R., Rezaei, F., Vahdani, A., (2017), Dust particles and their impact on air quality, Fourth International Conference on Environmental Planning and Management, pp. 36-41.

Ataee, S., Mohammadzadeh, A., Abkar, A., (2015), Dust Detection Using the Decision Tree Method from the Images of the Judge, Science and Mapping Techniques, 4(4), pp.151-161. [In Persian]

Babaee, P., Safarrud, T., Karimi, M., (2016), Analysis and Identification of Patterns of Blight Dust in the West of Iran, Geography and Environmental Hazards, 5(17), pp.105-119. [In Persian]

Bagheri, Z., Mahmoud, H., (2016), Synoptic Analysis of Dust in Extensive 52 South Iran, First International Conference on Natural Hazards and Environmental Crises in Iran, Solutions to Challenges, Pp 26-29.

Boroumand, P., (2016), Determination of dust particles by studying their physical and chemical properties and numerical modeling in Masjed Soleiman city, Health and Environment, 9(4), pp.517-526. [In Persian]

Dastjerdi, J., Mousavi, H., Kashki, A., (2011). An Analysis of Ilam Dust Dust Storm, Geography and Environmental Planning, Year 23, 46(2), pp.15-34. [In Persian]

Fallah, M., Vafai Nejad, A., Khairkh, M., Ahmadi, F., (2014), Monitoring and Synoptic Analysis of Dust, Geographic Information, 23(91), pp.69-80. [In Persian]

Field, G., Bahrami, M., Mohammadi, N., (2017), Dust storm tracking (Case study of Khuzestan), The first conference of ideas and technologies in geography, pp. 11-16.

Hajibarpour, Q., Mohammadi, S., Akbarnia, A., Ahadi, M., (2015), Synoptic and Statistical Analysis of Dust Dust in Ardabil, 5th Regional Climate Change Conference, pp. 13-17.

kanarkohi, H., Riahi, M., and Zahra, M., (2010). Using the Intelligent Neuro-Fuzzy Inference Inventory (ANFIS) system to predict the human papillomavirus's cancer-causing potential. Journal of Arak University of Medical Sciences, 4(2), pp.95-105. [In Persian]

kandoomkar, A., Fanieh, R., Daneshvar, F., Kardan, H., Ahdynejad, M., Rezaei; N., (2017), Investigation and correlation of temperature series trends and dust days in Hamedan province, Geography, 15(53), pp.277-293. [In Persian]

Karimi, M., Kamyar, N., (2011). Interaction between the flow of the atmosphere and the surface of the earth in the mechanism of the formation and expansion of Middle Eastern dust storms, Natural Geography Research, 43(78), pp.113-130. [In Persian]

Khoshkiysh, A., Alijani, B., Hejazizadeh, Z., (2011). Synoptic Analysis of Dust Systems in Lorestan Province, Applied Geosciences Research, 18(21), pp.91-110. [In Persian]

koshaklaq, F., Najafi, M., Mohammad, Z., Shirazi, M., Samadi, M., (2013), Investigating the Dust Composition Composition in the West and Southwest of Iran, Geography and Environmental Hazards, 2(6), pp.17-63. [In Persian]

koshaklaq, F., Najafi, M., Samadi, M., (2012), Survey on the occurrence of spring dust in western Iran, Natural Geography Research, 44(2), pp.99-124. [In Persian]

Makvandi, R., Bijan, M., Mohammadfam, I., (2012). Utilization of TOPSIS Multivariate Decision Making Model in Environmental Impact Assessment of Oil Refineries (Case Study: Khuzestan Super Heavy Oil Refinery), Environmental Researches, 3(5), pp.77-86. [In Persian]

Miri, M., Azizi, G., Nabavi, O., (2011). Detection of Dust in the Midwest of Iran, Geographical Studies in Arid Regions, 2(7), pp.63-83. [In Persian]

Naserpour, S., Alijani, B., Rezaiyan, P., (2015), The Effect of Dust Storms in Southwest of Iran Using Satellite Images and Air Maps, Natural Geography Research, 47(1), pp.21-36. [In Persian]

Nazmfar, H., Ali Bakhshai, A., (2014), Measurement of Spatial Inequality in Using Educational Indices Using Topsis Method (Case Study: Khorestan Province), Two Chapters of Educational Planning Studies, 3(6), pp.115-134. [In Persian]

Safarian zengir, V., Zainali, B., Jafari, Y., Jafarzadeh, L., (2018), Dust and Dust Estimation in Ardabil Province Using ANFIS Model, Environmental Impact Analysis Spatial Analysis Journal, 5(2), pp.125-142. [In Persian]

Shamsipour, A., Taher, S., (2012), Satellite analysis of dust phenomena, Natural Geography Research, 44(79), pp.111-126. [In Persian]

Tohidi, A., Zareemhahjerdi, M., Mehrabi, H., NasibAbdipour, H., (2015), Estimation of Hybrid Model of Artificial Neural Network-Data Panel in Determining Dry Dry Export Prices of Iran, Journal of Quantitative Economics (ex-economic studies), 12(3), pp.95-116. [In Persian]

Zohourian, M., Jahangiri, L., (2014), Synoptic and Statistical Analysis of Dust Events in Ahwaz, First National Conference on Environmental Pathology and Urban Pollution, pp. 69-67.

Zolfaghari, H., Masoompour, J., ShayeganMehr, S., Ahmadi, M., (2011). Survey of Dust Dust Storms in Iran during 2005-2007, Geography and Environmental Planning, 22(3), pp.17-34. [In Persian]

References (in English)

Arnas, C; lrby, J; Celli, S; detemmerman, G; Addab, Y; couedel, L; grisolia, C; lin, Y; martin, C; pardanaud, C; pierson, S., (2017), Characterization and origin of large size dust particles produced in the alcator _C mod tokamak, Nuchear materials and energy, 11, (12-19).

Baltagi, B.H., (2005), Econometric Analysis of Panel Data. 3rd Edition, New York: John Wiley and Sons.

Cantarella, G.E., & Luca, S.D.E., (2005), Multilayer Feedforward Networks for Transportation Mode Choice Analysis: An Analysis and a Comparison with Random Utility Models. Transportation Research Part C: Emerging Technologies, 13, (121-155).

Co, H.C., & Boosarawongse, R., (2007), Forecasting Thailand’s Rice Export: Statistical Techniques Vs. Artificial Neural Networks. Computers & industrial engineering, 4, (610-627).

Cuevas, E., Gomezplelaz, A.G., rodriguez, S., terradellas, E., basart, S., Garcia, R.D; Garcia, O.E., Alonso, S., (2017), The pulsating nature of large scale Saharan dust transport, atmospheric environment, 167, (586-602).

Dansie, A. p., wigs, G.F.S., Thomas, D.S.G., Washington, R., (2017), Measurements of windblown dust characteristics and ocean fertilization potential, Aeolian research, 29, (30-41).

Gujarati, D.N., (2003). Basic econometrics. 4th edition, New York: McGraw-Hill.

Jixia, H., qibin, Z., jing, T., depeng, Y., quansheng, G., (2017), Association between forestry ecological engineering and dust weather in lnner Mongolia, physics and chemistry of the earth, parts A/B/C, 104, (76-83).

Kisi, O., Ozturk O., (2007), Adaptive neurofuzzy computing technique for evapotranspiration estimation. Journal of Irrigation and Drainage Engineering. ASCE, 4, (368-379).

Liu, Z; wang, D; miloshevsky, G., (2017), Simulation of dust grain charging under tokamak plasma conditions, nuclear materials and energy, 12, (530-535).

Nabavi, O., haimberger, L., samimi, C., (2017), Sensitivity of WRF_ chem predictions to dust source function specification in west asia, Aeolian research, 24, (115-131).

Sahu, O., rao, D., gabbiye, N., engidayehu, A., teshale, F., (2017), Sorption of phenol fram synthetic aqueous solution by activated saw dust, biochemistry and biophysics reports, 12, (46-53).

Shoji, M., kawamura, G., smirnov, R., pigarov, A., tanaka, Y., masuzaki, S., uesugi, y., (2017), Simulation of impurity transport in the peripheral plasma due to the emission of dust in long pulse discharges on the large helical device, nuclear materials and energy, 12, (779-785).

Wang, Z., pan, X., uno, I., li, J., wang, Z., chen, X., fu, P., yang, T., kobayashi, H., shimizu, A., sugimoto, N., yamamotom, S., (2017), Significant impacts of heterogeneous reactions on the chemical composition and mixing state of dust particles, atmospheric environment, 159, (83-91).

Willame, Y., Vandaele, A.C., depiesse, C., lefevre, F., letocart, V., Gillotay, D., montmessin, F., (2017), Retrieving cloud dust and ozone abundances in the martion atmosphere SPICAM/UV nadir spectra, planetary and space science, 142, (9-25).

Wooldridge, J.M., (2006), Introductory Econometrics: A Modern Approach. 3rd Edition, New York: South-Western.

Zalesna, E., grzonka, J., rubel, M., carrasco, A., widdowson, A., baron, A., ciupinski, L., contributors, J., (2017), Studies of dust from JET with the ITER like wall: composition and internal structure, nuclear materials and energy, 12, (582-587).

Zielhofer, C., suchodoletz, H., fletcher, W., Schneider, B., dietze, E., schleget, M., schepanski, K., weninger, B., mischke, S., mikdad, A., (2017), millennial scale fluctuations in Saharan dust supply across the decline of the African humid period, quatemary science reviews, 717, (119-135).