Modeling and Prediction of Dust in Western Iran

Document Type : Full length article


1 Professor of Physical Geograpgy, Department of Physical Geography, Climatology, University of Mohaghegh Ardabili, Ardabil, Iran

2 PhD Student, Department of Physical Geography, Climatology, University of Mohaghegh Ardabili, Ardabil, Iran

3 PhD Student, Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran


Dust in hazardous areas anywhere in the world is harmful for human societiesand life organisms.  Dust Mineral Aerosols can significantly affect Earth's climate (Zhiyuan et al., 2019: 3). The prevalence of dust storms is devastating human health and agricultural activities in Central Asia (Tiangang et al., 2019: 16). Dust plays an important role in socio-economic development, but on the other hand, such supply can have a negative impact on the environment of the forest (Narayan et al, 2019: 4). According to the previous studies, the importance of dust and the resulting hazard can show that the dust parameter is important for natural hazards. According to the studies, the existing methods for studying the dust that has been done so far have been general and have not adequately addressed the subject. Dust in the areas under its control anywhere in the world has had a risk for various parts of life. Dust storms have also been growing in recent years (Mohammad Khan, 2017: 495). Dust phenomenon due to recent droughts caused adverse biological effects and damages in agriculture, industry and transportation in the provinces of Khuzestan and other neighboring areas (Darvishi et al., 2017: 1). Today, dust is one of the common phenomena and is one of the major environmental problems in arid and semi-arid areas (Hejazi Zadeh et al., 2018: 108). The purpose of this study is to analyze the dust data first to address this issue and then, using ANFIS and RBF models, to make a modeling comparison. Finally, the results can predict for a better view of the dust situation for the future, in dusty regions of Iran.
Material and methods
In this study, after analyzing 29-year-old dust data for 28 stations the regions afflicted by the phenomena in Iran, they were first analyzed and then normalized. After normalizing the dust data using two new and powerful applied models for modeling and forecasting in climateology, the ANFIS and RBF models were modeled. Then, the two models were compared for accurate prediction of the future, and after training the dust data, they were predicted for the coming years. Finally, using the TOPSIS multivariate decision making model, regions are more involved with the priority hazardous dust hazard phenomenon and by utilizing ArcGIS software output data.
RBF neural network model
Neural networks with radial base function are widely used for nonparametric multi-dimensional functions through a limited set of training information. Radial neural networks with a fast and comprehensive learning are very interesting and efficient, and they pay particular attention to it, Hartman et al. (1990). Gyrosy, Pogni, as well as Hartmann and Kepler, in the 1990s proved that radial-basis grid networks are very powerful approximation devices, so that by having a sufficient number of hidden neurons, they can be able to approximate each function accurately with every degree. These networks are often compared to the neural network back propagation error. The main architecture of the RBF consists of a two-tier network (Khanjani et al., 2016).
ANFIS Neural Network Model
In this step, it is possible to model and predict dust in the study area using the ANFIS comparative neuro-fuzzy network model (Ansari, 2010: 29). In this study, the phenomenon of dust in a series of time of 276 months (23 × 12 276) was considered in two ANFIS and RBF neural networks models in each station. In a time series consisting of n examples x_1, 〖x〗 _ (2), ..., x_n is the next value of relation (6) of its previous value (Asghari Oskouei, 2002: 75).  
The fuzzy system is based on the "conditional-result" logical rules that, using the concept of linguistic variables and fuzzy decision making process depicts the space of input variables on the space of the output variables. The combination of fuzzy systems based on logical rules, and artificial neural network methods can enable the extraction of knowledge from numerical data. It has led to the introduction of a comparative neural system inference. A sogeven fuzzy system was presented with three inputs, one output and two laws and an equivalent ANFIS system. This system has two inputs x and y and one f output.
Proximity to Ideal Mode (TOPSIS)
Huang and Yun proposed TOPSIS in 1981. In this method, m options (A1, A2, ..., Am) were evaluated with n indices (C1, C2, ..., Cn) (Momeni, 2008). Solving this problem with this method was carried out in the following steps (Makvandi et al., 1391; Law and Order, 2014).
Results and discussion
The zoning of dust phenomena in dusty regions of Iran using TOPSIS
The results of the implementation of the Topsis model, using the degree of importance of the criteria derived from the entropy method, indicate that, in terms of dust intensity, places more and less dusty for the next 14 years in dusty areas Iran, three stations of Abadan, Masjed Soleyman and Ahvaz were exposed to dust (1, 95%, and 81%), respectively, for simulated years. The northern stations of the study area including Khoy, Boroujen and Ahar showed a lower dust intensity with percentages (0.1, 0.4 and 0.6), respectively. According to TOPSIS model, south west and west of Iran were exposed to dust for simulated years.
According to comparisons of ANFIS and RBF neural network models, the two models were trained to predict dust. The results obtained from the training of the ANFIS neural network model at best, the RMSE value was 11.67 and the R2 value was 0.5879. But the results obtained from the training of the RBF neural network model, at best, were RMSE equal to 2.19 and the R2 value was 0.9854. By comparing these two models, it was finally concluded that the performance of the RBF neural network model was better. According to the modeling and the results obtained from the comparison of the models, the accuracy and reliability of the RBF neural network model was confirmed for prediction, then it was used modelling in this study.


احمدزاده‏، ‏ک.؛ لطفی، م. و محمدی، ک. (1389). مقایسة سیستم‏های هوش مصنوعی در ANN و ANFIS در تخمین میزان تبخیر تعرق گیاه مرجع در مناطق بسیار خشک ایران، نشریة آب و خاک، 4(5): ۶۷۹-689.
اصغری ‏اسکویی، م. (1381). کاربرد شبکه‏های عصبی در پیش‏بینی سری‏های زمانی. فصل‏نامه پژوهش‏های اقتصادی ایران، 12(4): ۷۹-۹9.
انصاری، ح. داوری، ک. (1386). پهنه‏بندی دورة خشک با استفاده از شاخص بارندگی استانداردشده در محیط GIS (مطالعة موردی: استان خراسان)، پژوهش‏های جغرافیایی، 60(3): ۷۹-108.
جلالی، ن.؛ ایران‏منش، ف. و داودی، م. (1396). شناسایی منشأ و مناطق تحت تأثیر طوفان‏های گرد و غبار در جنوب غرب ایران با استفاده از تصاویر مادیس، نشریة مهندسی و مدیریت آبخیر، 9(4): ۲۱۸-331.
حجازی‏زاده، ز.؛ طولابی‏نژاد، م.؛ زارعی، ز. و امرایی، ب. (1397). پایش طوفان گرد و غبار در نیمة غربی ایران، مطالعة موردی: طوفان گردوغباری 16 تا 19 ژوئن 2015،نشریة تحلیل فضایی مخاطرات محیطی، 4(8): ۱۰۷-124.
حسینی، ا. و رستمی د. (1397). واکاوی و ردیابی پدیدة گرد و غبار در جنوب و جنوب شرق ایران با استفاده از مدل Hysplit و اصول سنجش‏ از دور،نشریة تحلیل فضایی مخاطرات محیطی، 3(9): 10۳-10۹.
خانجانی، ط.؛ عطایی، م. و معلم پ. (1395). پیش‏بینی سرعت باد با شبکة عصبی RBF براساس نظریة آشوب، هوش محاسباتی در مهندسی برق، 3(5): ۸۷-96.
درویشی، ج.؛ عباس‏قلی، ف. و و محمدی ع. (1396). کانی‏شناسی و ژئوشیمی رسوبی گردوغبارهای وارده به استان خوزستان، مخاطرات محیط طبیعی، 14(9): 1-۱۶.
رایگانی، ب. و خیراندیش، ز. (1396). بهره‏گیری از سری زمانی داده‏های ماهواره‏ای به‏منظور اعتبارسنجی کانون‏های شناسایی‏شدة تولید گرد و غبار استان البرز،نشریة تحلیل فضایی مخاطرات محیطی، 4(2): 1-۱۸.
رفیعی، ز.؛ یزدانی، م. و رحیمی، م. (1395). تحلیل روند تعداد روزهای همراه با گرد و غبار در ایران، دو فصل‏نامه علمی-پژوهشی خشک بوم، 2(4): ۱۱-۲۳.
سبحانی ب،؛ صفریان­زنگیر و. (1398). واکاوی و پیش‌بینی پدیده گرد و غبار در جنوب غرب ایران، مخاطرات محیط طبیعی، 8(22): 179-198.
سبحانی، ب. و صفریان ‏زنگیر، و. (1397). بررسی و پیش‏بینی اثرات مخاطره‏ای دمای فرین ماهانه بر روی محصولات باغی و کشاورزی در نوار شمالی ایران (استان‏های گلستان، گیلان، و مازندران)،نشریة تحلیل فضایی مخاطرات محیطی، 4(8): ۱۲۵-144.
سبحانی، ب.؛ جعفرزاده­علی­آباد، ل. و صفریان­زنگیر، و. (1398). مدل‌سازی، تحلیل و پیش بینی پدیده­ی خشکسالی در ایران، هیدروژئومورفولوژی، 6(21): 181-202.
صحرایی، ج.؛ بهرامی، م. و محمدی، ن. (1396). ردیابی طوفان گرد و غبار (مطالعة موردی: خوزستان)، اولین همایش اندیشه‏ها و فناوری‏های نوین در علوم جغرافیا، 1۱-1۶.
صفریان ‏زنگیر، و.؛ زینالی، ب.؛ جعفری، ی. و جعفرزاده، ل. (1397). بررسی گرد و غبار و ارزیابی امکان پیش‏بینی آن در استان اردبیل با استفاده از مدل ANFIS، نشریة تحلیل فضایی مخاطرات محیطی، 2(7): ۱۲۵-142.
صفریان­زنگیر، و.؛ سبحانی، ب. و اصغری، ص. (a1398). مدل‏ سازی و پایش پدیده خشک‌سالی در جنوب غرب ایران با استفاده از شاخص جدید فازی، پژوهش­های جغرافیای طبیعی، 51(4): 673-692.
صفریان­زنگیر، و.؛ سبحانی، ب. و رضائی­بنفشه، م. (b1398). مدل سازی و پایش پدیده خشکسالی در شمال غرب ایران، جغرافیا و مخاطرات محیطی، 8(31): 1-13.
عمارلو، ج.؛ جاوید، ح.؛ شکاریان، ر.؛ رضایی، ف. و وحدانی، ا. (1396). ذرات گرد و غبار و تأثیر آن بر کیفیت هوا، چهارمین کنفرانس بین‏المللی برنامه‏ریزی و مدیریت محیط زیست، 41-36.
کارگر، ا.؛ جمالی، ج.؛ رنجبر، ع.؛ معین‏الدینی، م. و گشتاسب، ح. (1395). شبیه‏سازی و تحلیل عددی طوفان گرد و غبار شدید شرق ایران،نشریة تحلیل فضایی مخاطرات محیطی، 4(2): ۱۰۱-119.
کنارکوهی، ع.؛ سلیمان‏جاهی، ح.؛ فلاحی، ش.؛ ریاحی‏مدوار، ح. و مشکات، ز. (1389). استفاده از سیستم جدید هوشمند استنتاج فازی‏- عصبی تطابقی ANFIS برای پیش‏بینی قدرت سرطان‏زایی ویروس پاپیلوهای انسانی، مجلةعلمی پژوهشی دانشگاه علوم پزشکی اراک، 4(6): ۹۵-105.
گندمکار، ا.؛ فنایی، ر.؛ دانشور، ف.؛ کاردان، ح.؛ احدی‏نژاد م. و رضایی، ن. (1396). بررسی و ارتباط‏سنجی روند سری‏های دمایی و روزهای همراه با گرد و غبار استان همدان. جغرافیا، 53(8): ۲۷۷-293.
محمدخان، ش. (1396). بررسی وضعیت و روند تغییرات طوفان‏های گردوغباری در ایران در دورة زمانی 1364 الی 1384، مرتع و آبخیزداری، مجلة منابع طبیعی ایران، 2(3): 514-495.
مکوندی، ر.؛ مقصودلو ‏کمالی، ب. و محمدفام، ا. (1391). بهره‏مندی از مدل تصمیم‏گیری چندمعیارة TOPSIS در ارزیابی پیامدهای محیط زیستی پالایشگاه‏های نفت (مطالعة موردی: پالایشگاه نفت فوق سنگین خوزستان)، پژوهش‏های محیط‏زیست، 3(4): ۷۷-86.
نصیری، ب.؛ زارعی، ز.؛ حلیمی، م. و رستمی، م. (1395). بررسی تغییرات ارتفاع و ضخامت لایة مرزی در شرایط گردوغباری شهر اهواز، نشریة تحلیل فضایی مخاطرات محیطی، 2(8): ۵۱-64.
نظم‏فر، ح. و علی‏بخشی،‏ آ. (1393). سنجش نابرابری فضایی در برخورداری از شاخص‏های آموزشی با استفاده از روش تاپسیس (مطالعة موردی: استان خورستان)، دو فصل‏نامة مطالعات برنامه‏ریزی آموزشی، 3(6): ۱۱۵-134.
ولی، ع. و روستایی، ف. (1396). بررسی روند فرسایش بادی در ایران مرکزی با استفاده از شاخص طوفان گرد و غبار در پنجاه سال اخیر، نشریة علوم آب و خاک (علوم و فنون کشاورزی و منابع طبیعی)، 4(6): ۱۸۹-200.
Ahmadzadeh Gheghighi Giz-Qavveh, M. and Mohammadi, K. (2010). Comparison of Artificial Intelligence Systems (ANN and ANFIS) in Estimating the Rate of Evapotranspiration of Reference Plants in Iran's High Drylands, Water and Soil Journal, 4(5): 689-679. [In Persian].
Amarlou, J.; Javid, H.; Shagharian, R.; Rezaei, F. and Ondani, A. (2017). Dust particles and their impact on air quality, Fourth International Conference on Environmental Planning and Management, 36-41. [In Persian].
Ansari, H. and Davar, K. (2007). Dry zone zoning using standardized rainfall index in GIS environment (Case study: Khorasan province), Geographical research, 60(3): 79-108. [In Persian].
Arnas, C.; Celli, J.; Detemmerman, S.; Addab, G.; Couedel, Y.; Grisolia, L.; Lin, C.; Martin, Y.; Pardanaud, C. and Pierson, C. (2017). Characterization and origin of large size dust particles produced in the alcator C_ mod tokamak, Nuchear materials and energy, 3(11): 12-19.
Asghariaskoy, M. (2002). Application of Neural Networks in prediction of time series, Journal of Economic Research, 12(4): 79-99. [In Persian].
Cuevas, E.; Plelaz, G.; Rodriguez, A.G.; Terradellas, S.; Basart, E.; Garcia, S.; Garcia, R.D. and Alonso, O.E. (2017). The pulsating nature of large scale Saharan dust transport, Atmospheric environment, 11(167): 586-602.
Dansie, A.; Wigs, S.; Thomas G.; and Washington, D. (2017). Measurements of windblown dust characteristics and ocean fertilization potential, Aeolian research, 4(29): 30-41.
Darwishi, C.; Abbasgoli, F. and Mohammadi, A. (2017). Mineralogy and sedimentary geochemistry of dust entering Khuzestan province, Environmental hazards, 14(9): 1-16. [In Persian].
Ghouse, B.; Venkat, M.; Ratnama, K.; Niranjan, K.; Kishored, P. and Isabella, V. (2019). Long-term variation of dust episodes over the United Arab Emirates, Journal of Atmospheric and Solar-Terrestrial Physics, 7(187): 33-39.
Hartman, E.; Keeler, J.D. and Kowalski, J.M. (1990). Layered neural networks with Gaussian hidden units as universal approximations, Neural Computation, 8(2): 210-215.
HejaziZadeh, Z.; Probbaynejad, M.; Zarei, Z. and Amani, B. (2018). Dust storm monitoring in the western part of Iran, Case study: Dust storm June 16-19, 2015, Environmental spatial analysis publication, 4(8): 107-124. [In Persian].
Hosseini, A. and Rostami, D. (2018). Detection and tracing of dust phenomena in south and south-east of Iran using the Hysplit model and the principles of remote sensing, Environmental spatial analysis of environmental hazards, 3(9): 103-109. [In Persian].
Jalali, N.; IranManshe, F. and Davoodi, M. (2017). Identification of the origin and areas affected by dust storms in southwestern Iran using the images of the mother, Beheshir Engineering & Management Journal, 9(4): 218-331. [In Persian].
Jixia, H.; Zhang, Q.; Tan, J.; Yue, D. and Quansheng, G. (2017). Association between forestry ecological engineering and dust weather in lnner Mongolia, Physics and chemistry of the earth, 36(12): 14-27. 
Kanarkhui, A.S.; Sulayman-yeah, H.; Falahi, Sh.; Rhyamodavr, H. and Meskat, Z. (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(6): 95-105. [In Persian].
Kandomkar, A.; Fanayi, R.; Daneshvar, F.M. and Rezaei, N. (2017). Investigation and connection of the process of temperature series and days with dust in Hamedan province, Geography, 53(8): 277-293. [In Persian].
Karkar, A.; Jamali, C.; Ranjbar, A.; Mina al-Dini, M. and Goshtasb, H. (2017). Simulation and numerical analysis of the storm of severe dust in eastern Iran, Environmental Spatial Spatial Analysis Journal, 4(2): 101-119. [In Persian].
Khanjani, T.; Atay, M. and Molam, P. (2016). Estimation of wind speed with RBF neural network based on chaos theory, Computational intelligence in electrical engineering, 3(5): 87-96. [In Persian].
Liu, Z.; Dezhen, W. and Gennady, M. (2017). Simulation of dust grain charging under tokamak plasma conditions, Nuclear materials and energy, 5(12): 530-535.
Lu, M.; Xinghua, Y.; Tianliang, Z.; Qing, H.; Lua, H.; Ali, M.; Wen, H.; Fan, Y. and Chong, L. (2019). Modeling study on three dimensional distribution of dust aerosols during a dust storm over the Tarim Basin, Northwest China, Atmospheric Research, 2(218): 285-295.
Makvandi, R.; Maghsudulli Kamali, B. and Mohammadfam, A. (2012). Utilization of TOPSIS Multivariate Decision Making Model for Assessing the Environmental Impact of Oil Refineries (Case Study: Khuzestan Extra Heavy Oil Refinery), Environmental Research, 3(4): 77-86. [In Persian].
Mohammad Khan, Sh. (2017). The study of the status and trend of changes in dust storms in Iran during the period from 1985 to 2005. Irrigation and Watershed Management, Iranian Journal of Natural Resources, 2(3): 495-514. [In Persian].
Nabavi, O.; Leopold, H. and Cyrus, S. (2017). Sensitivity of WRF_ chem predictions to dust source function specification in west asia, Aeolian research, 14(24): 115-131.
Najafi, B. and Faizollahzadeh, S. (2018). Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC). Resources, Conservation & Recycling, 14(133): 169-78,
Narayan, K.; Khanindra, P.; Abhisek, C.; Subodh, K.; Chowdary, V.M.; Satiprasad, C.P.; Singh, S. and Samrat, B. (2019). Assessment of foliar dust using Hyperion and Landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas, Journal of Cleaner Production. 4(19): 30-33.
Nasiri, B.; Zarei, Z.; Halimi, M. and Rostami, M. (2016). Investigating changes in the height and thickness of the boundary layer in dusty conditions in Ahvaz city, Environmental Spatial Spatial Analysis Journal, 2(8): 51-64. [In Persian].
Nazmifar, H. and Causality, A. (2014). Measurement of Spatial Inequality in Using Educational Indices Using Topsis Method (Case Study: Khordestan Province), Two Chapters of Educational Planning Studies, 3(11): 115-134. [In Persian].
Rafiei, Z.; Yazdani, M. and Rahimi, M. (2016). The trend analysis of the number of days with dust in Iran, Two Quarterly Journal of Research and Development of Boom, 2(4): 11-23. [In Persian].
Raighani, B. and Kheyrandish, Z. (2017). Utilization of satellite data time series to validate the identified sources of dust production in Alborz province, Environmental spatial analysis, 4(2): 1-18. [In Persian].
Safarian Zengir, V.; Zeinali, B.; Jafari, Y. and Jafarzadeh, L. (2018). Dust analysis and assessment of its prediction in Ardabil province using ANFIS model, Environmental spatial analysis, 2(7): 125-142. [In Persian].
Safarianzangir, V.; Sobhani B. and Rezaeibanafsheh, M. (2019b). Modeling and monitoring of drought phenomenon in northwestern Iran, geography and environmental hazards, 8 (31): 1-13. [In Persian].
Safarianzangir, V; Sobhani B. and Asghari, S. (2019a). modeling and monitoring of drought phenomenon in southwestern Iran using the new fuzzy index, Natural Geographical Research, 51 (4): 673-692. [In Persian].
Safarianzengir, V.; Sobhani, B. (2020). Simulation and Analysis of Natural Hazard Phenomenon, Drought in Southwest of the Caspian Sea, IRAN, Carpathian Journal of Earth and Environmental Sciences, Vol. 15, No. 1, p. 127 - 136; DOI:10.26471/cjees/2020/015/115
Safarianzengir, V; Sobhani, B. and Asghari, S. (2019). Modeling and Monitoring of Drought for forecasting it, to Reduce Natural hazards Atmosphere in western and north western part of Iran, Iran. Air Qual Atmos Health (2019) doi:10.1007/s11869-019-00776-8
Sahrai, C.; Bahrami, M. and Mohammadi, N. (2017). Dust storm tracking (Khuzestan case study). The First Contemporary Thoughts and Technologies in Geography, 11-16. [In Persian].
Sahu, O.; Dubasi, R.; Nigus, G.; Addis, E. and Firomsa, T. (2017). Sorption of phenol fram synthetic aqueous solution by activated saw dust, Biochemistry and biophysics reports, 8(12): 46-53.
Shoji, M.; Kawamura, G.; Smirnov, R.; Pigarov, A.; Tanaka, Y.;  Masuzaki, S. and 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, 14(12): 779-785.
Sobhani B.; Safarianzangir V. (2019). Analysis and forecasting of dust phenomenon in southwestern Iran, hazards of natural environment, 8 (22): 179-198. [In Persian].
Sobhani, B. and Safarianzingir, V. (2018). Investigating and predicting the risk of monthly rainfed exposure to horticultural and agricultural products in the northern strip of Iran (Golestan, Gilan and Mazandaran provinces, Environmental spatial analysis, 4(8): 125-144. [In Persian].
Sobhani, B., Jafarzadehaliabad, L. and Safarianzengir, V. (2020a). Investigating the effects of drought on the environment in northwestern province of Iran, Ardabil, using combined indices, Iran. Model. Earth Syst. Environ. (2020).
Sobhani, B., Safarianzengir, V. (2020). Evaluation and zoning of environmental climatic parameters for tourism feasibility in northwestern Iran, located on the western border of Turkey, Modeling Earth Systems and Environment, (2020).
Sobhani, B.; Jafarzadehalialiabad, L. and Safarianzangir, V. (2019). Modeling, Analysis and Forecasting of Drought Phenomenon in Iran, Hydrogeomorphology, 6 (21): 181-202. [In Persian].
Sobhani, B.; Safarianzengir V. and Kianian, M.K. (2019a). Drought monitoring in the Lake Urmia basin in Iran. Arabian Journal of Geosciences 12:448.
Sobhani, B.; Safarianzengir V. and Kianian, M.K. (2019b). Modeling, Monitoring and Prediction of Drought in Iran. Iranian (Iranica) Journal of Energy and Environment 10: 216 - 224. doi: 10.5829/ijee.2019.10.03.09
Sobhani, B.; Safarianzengir, V. (2019a). Modeling, monitoring and forecasting of drought in south and southwestern Iran, Iran. Modeling Earth Systems and Environment 5:
Sobhani, B.; Safarianzengir, V. (2019b). Investigation hazard effect of monthly ferrrin temperature on agricultural products in north bar of Iran. Iraqi Journal of Agricultural Sciences. 50 (1): 320-330
Sobhani, B.; Safarianzengir, V. and Kianian, MK. (2018). Potentiometric Mapping for Wind Turbine Power Plant Installation guilan province in Iran. J. Appl. Sci. Environ. Manage 22: 1363 –1368.
Sobhani, B.; Safarianzengir, V. and Miridizaj, F. (2019c). Feasibility study of potato cultivating of Ardabil province in Iran based on VIKOR model. Revue Agriculture. 10 (2): 92 – 102.
Sobhani, B.; Safarianzengir, V. and Yazdani, M.H. (2020b) Modelling, evaluation and simulation of drought in Iran, southwest Asia. J Earth Syst Sci 129, 100 (2020).
Tiangang, Y.; Siyu, C.; Jianping, H.; Xiaorui, Z.; Yuan, L.; Xiaojun, M. and Guolon, Z. (2019). Sensitivity of simulating a dust storm over Central Asia to different dust schemes using the WRF-Chem model, Atmospheric Environment. 15(207): 16-29.
Vali, Ah. and Rural, F. (2017). The study of the trend of wind erosion in central Iran using the dust storm index in the last fifty years, Journal of Soil and Water Sciences (Science and Technology of Agriculture and Natural Resources), 4(6): 189-200. [In Persian].
Wang, Z.; Xiaole, P.; Itsushi, U.; Jie, L.; Zifa, W.; Xueshun, C.; Pingping, F.; Ting, Y.; Hiroshi, K.; Atsushi, S.; Nobuo, S. and Shigekazu, Y. (2017). Significant impacts of heterogeneous reactions on the chemical composition and mixing state of dust particles, Atmospheric environment, 3(159): 83-91.
Wei, P.; Qi, S.; Feng, X. and Yueyuan, J. (2018). Simulations of the dust behavior in the sampling and dust filters in the primary loop of HTR-10, Nuclear Engineering and Design, 9(302): 112-121.
Willame, Y.; Vandaele, A.C.; Depiesse, C.; Lefevre, F.; Letocart, V.; Gillotay, D. and Montmessin, F. (2017). Retrieving cloud dust and ozone abundances in the martion atmosphere SPICAM/UV nadir spectra, Planetary and space science, 7(142): 9-225.
William, G.; Tobin, M.; David, J. and Zach, U. (2018). Trajectory measurements for individual dust particles on the colorado dust Accelerator. Nuclear Inst. and Methods in Physics Research, 10(908): 269-276.
Zalesna, E.; Grzonka, J.; Rubel, M.; Carrasco, A.; Widdowson, V.; Baron, A.; Ciupinski, G. and Contributors, L. (2017). Studies of dust from JET with the ITER like wall: composition and internal structure, Nuclear materials and energy, 8(12): 582-587.
Zhiyuan, H.; Jianping, H.; Chun, Z.; Jiangrong, B.; Qinjian, J.; Yun, Q.; Ruby, L.; Taichen, F.; Siyu, C. and Jianmin, M. (2019). Modeling the contributions of Northern Hemisphere dust sources to dust outflow from East Asia. Atmospheric Environment, 6(14): 1352-2310.
Zielhofer, C.; Hans, S.; William, F.; Birgit, S.; Elisabeth, D.; Michael, S.; Kerstin, S.; Bemhard, W.; Steffen, M. and Abdeslam, M. (2017). millennial scale fluctuations in Saharan dust supply across the decline of the African humid period, Quatemary science reviews, 4(171): 119-135.