In recent years, the damage caused by dust in different parts has increased dramatically. There are many dusty areas around the world. One of these regions in the southwest of Asia is Iran. The purpose of this study is to model and predict the hazardous dust phenomenon in dusty regions of Iran. For this purpose, dust data from 28 stations of intense dusty areas in Iran were collected at 29-year time intervals. Then, adaptive-network-based fuzzy inference systems (ANFIS) and the core of the radial base function (RBF) models were used for modelling and then the two models were compared to the future exact prediction. Finally, the dust data for all stations are prioritized using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multivariate decision-making model and output data are mapped by ArcGIS software. According to the results of this study, RMSE of the ANFIS model was 10.5 and the RBF model was 2.18. Therefore, the accuracy of RBF was more than the ANFIS model for prediction of the dust simulated in years, so the RBF model was used to predict. Based on the dust data obtained from the output of the RBF model in both the mean and maximum of dust abundance, the western and southwestern stations of the study areas were more exposed to dust in the future. Also, according to TOPSIS model, in the prioritization of stations involved with dust for simulated years, Abadan, Masjed Soleyman and Ahwaz were ranked by the amount of 100, 95% and 81%, respectively. Dust is one of the atmospheric phenomena that have adverse environmental effects and consequences. Dust storms have detrimental effects on the health and economy of society and climate change. Understanding the nature, origin and effects of dust storms plays an important role in determining its control methods.

中文翻译：

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使用遥感和 GIS 监测和预测伊朗西半部以及土耳其和伊拉克东部边界的粉尘并调查其环境影响

近年来，灰尘对不同部位造成的损害急剧增加。世界上有许多尘土飞扬的地区。亚洲西南部的这些地区之一是伊朗。本研究的目的是模拟和预测伊朗多尘地区的危险粉尘现象。为此，以 29 年的时间间隔收集了来自伊朗强尘区 28 个站点的尘埃数据。然后，使用基于自适应网络的模糊推理系统（ANFIS）和径向基函数（RBF）模型的核心进行建模，然后将这两种模型与未来的精确预测进行比较。最后，使用与理想解相似的顺序偏好技术（TOPSIS）多元决策模型对所有站点的灰尘数据进行优先排序，并通过ArcGIS软件映射输出数据。根据本研究结果，ANFIS模型的RMSE为10.5，RBF模型为2.18。因此，RBF 模型对多年模拟的沙尘预测精度要高于ANFIS 模型，因此采用RBF 模型进行预测。根据RBF模型输出得到的沙尘丰度平均值和最大沙尘数据，未来研究区西部和西南台站受沙影响较大。此外，根据 TOPSIS 模型，在模拟年份涉及沙尘的台站的优先排序中，Abadan、Masjed Soleyman 和 Ahwaz 分别以 100、95% 和 81% 的数量排名。灰尘是具有不利环境影响和后果的大气现象之一。沙尘暴对社会的健康和经济以及气候变化产生不利影响。