مدل‏ سازی و پیش ‏بینی گرد و غبار در غرب ایران

نوع مقاله: مقاله کامل

نویسندگان

1 استاد گروه جغرافیای طبیعی، اقلیم‌شناسی، دانشگاه محقق اردبیلی، اردبیل، ایران

2 دانشجوی دکتری گروه جغرافیای طبیعی، اقلیم‏شناسی، دانشگاه محقق اردبیلی، اردبیل، ایران

3 دانشجوی دکتری گروه مهندسی بیوسیستم، دانشگاه محقق اردبیلی، اردبیل، ایران

چکیده

برای مدل‏سازی و پیش‏یابی پدیدة مخاطره‏ای گرد و غبار در مناطق گردوغبارخیز ایران، نخست داده‏های گرد و غبار، دما، و رطوبت 28 ایستگاه مناطق درگیر شدید با گرد و غبار در ایران در بازة زمانی 29 ساله (2018-1990) اخذ شد. سپس، با استفاده از مدل‏های شبکة عصبی ANFIS و RBF در نرم‏افزار MATLAB مدل‏سازی‏ها انجام گرفت. داده‏های گرد و غبار به‏دست‏آمده از پیش‏بینی با استفاده از مدل تصمیم‏گیری چندمتغیرة TOPSIS و مناطق بیشتر درگیر با پدیدة مخاطره‏ای گرد و غبار برای سال‏های آتی اولویت‏سنجی و مشخص شدند. براساس نتایج پژوهش، مقایسة دو مدل شبکة عصبی ANFIS و RBF در بهترین شرایط نشان داد که مقدار RMSE مدل ANFIS برابر با 67/11 و مدل RBF برابر با 19/2 است. بنابراین، قدرت دقت RBF در پیش‏بینی گرد و غبار در سال‏های شبیه‏سازی‏شده بیشتر است. براساس نتایج خروجی مدل شبکه عصبی- مصنوعی RBF در پیش‌بینی گرد و غبار برای سال‏های آتی ایستگاه­های مورد مطالعه؛ در هر دو مقیاس میانگین و حداکثر فراوانی گرد و غبار، ایستگاه‏های غربی و جنوب غربی منطقه مورد پژوهش بیشتر در معرض گرد و غبار در سال­های آینده قرار گرفتند. همچنین، در مدل TOPSIS، ایستگاه‏های آبادان، مسجد سلیمان، و اهواز به‏ترتیب با مقدار درصد (1، 95/0، و 81/0) در معرض گرد و غبار قرار گرفتند.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling and prediction of dust in western Iran

نویسندگان [English]

  • Behrooz Sobhani 1
  • Vahid Safarian Zengir 2
  • Sina faizollahzadeh 3
1 PHD. Department of Physical Geography University of Mohaghegh Ardabili
2 Ph.D. student of physical geography, climatology, University of Mohaghegh Ardabili
3 Ph.D. student, Dept. of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
چکیده [English]

Introduction
Dust in hazardous areas anywhere in the world is hazardous In different parts Life Organisms had. Dust Mineral Aerosols can significantly affect Earth's climate (Zhiyuan et al., 2019: 3). The prevalence of dust storms is devastating to human health and agricultural activities in Central Asia (Tiangang et al., 2019: 16). Dust it plays an important role in socio-economic development, but on the other hand, such supply can have a negative impact on the environment and the environment of the forest (Narayan et al, 2019: 4). According to the above studies, the importance of dust and the resulting hazard, in the case study, it can be admitted 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 dangers in various parts of life. Drought storms have also been growing in recent years (Mohammad Khan, 2017: 495). Drought phenomenon due to recent droughts has caused adverse biological effects and damages in agriculture, industry and transportation in the provinces of Khuzestan and other neighboring districts (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 was to analyze the dust data first to address this issue Then, using ANFIS and RBF models, a modeling comparison was used and finally, for a better view of the dust situation for the future, in dusty regions of Iran, they were predicted.

Material and method
In this study after analyzing 29-year-old dust data for 28 stations of dusty regions of Iran, they were first analyzed and then normalized, and non-normal stations were 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. and Then the two models were compared to accurate prediction for 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 Zoned.
RBF neural network model
Neural networks with radial base function are widely used for nonparametric multi-dimensional functions through a limited set of educational 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-base 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 Continuously and 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 studied 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) in two ANFIS and RBF neural networks models in each station was considered. 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).
(6) x_k=f (〖 x〗_(k-1 ),〖 x〗_(k-2 ),…,x_(k-p ))
The fuzzy system is a system 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 that enable the extraction of knowledge from numerical data, has led to the introduction of a comparative neural system inference. In Fig. 3, a sogeven fuzzy system with three inputs, one output and two laws and an equivalent ANFIS system were presented. 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).

Conclusion
Comparison of two ANFIS and RBF neural network models
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 the prediction of the RBF neural network model was used.

Results
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.

کلیدواژه‌ها [English]

  • simulation
  • Hazard
  • RBF and ANFIS models
  • Iran's dusty areas
  • Statistical analysis
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