نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 استادیار، گروه احیا مناطق خشک و کوهستانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

10.30467/nivar.2021.303747.1200

چکیده

در این پژوهش برای پیش‌بینی طوفان‌های گردوغبار، داده‌های ساعتی گردوغبار و داده‌های ماهانه دمای بیشینه، کمینه، میانگین، سرعت بیشینه باد و مجموع بارش در سه ایستگاه سینوپتیک آبادان، اهواز و بستان با طول دوره آماری 25 ساله (2014-1990) گردآوری شد. برای بررسی تأثیرپذیری طوفان‌های گردوغبار از نوسانات اقلیمی علاوه بر متغیرهای مذکور، شاخص خشک‌سالی استانداردشده بارش-تبخیر و تعرق (SPEI) نیز در پنجره زمانی فصلی محاسبه گردید. پیش‌بینی تعداد روزهای همراه با طوفان‌های گردوغبار در مقیاس فصلی با استفاده از چهار روش هوش مصنوعی شامل MLP، ANFIS، RBF و GRNN انجام شد که در قالب سه آزمایش شامل بررسی تأثیر افزودن ویژگی‌های کمکی بر روی پیش‌بینی، بررسی تأثیر تعداد فصل‌های گذشته در پیش‌‌بینی و بررسی بهترین تکنیک از بین مدل‌های استفاده‌شده مورد ارزیابی قرار گرفت. نتایج نشان داد که در تمامی ایستگاه‌ها، استفاده از همه ویژگی‌ها باعث بهبود پیش‌بینی گردوغبارشده است و مقدار شاخص میانگین قدر مطلق خطا (MAE) برای ایستگاه‌های آبادان، اهواز و بستان به ترتیب برابر با 1/15، 1/66 و 0/66 به دست آمد که همگی مربوط به فصل پاییز بودند. همچنین نتایج نشان داد که در ایستگاه‌ بستان، با فرض ثابت بودن داده‌های چهار فصل گذشته و استفاده از تمام ویژگی‌های ورودی، مدل ANFIS باعث می‌شود که خطای پیش‌بینی کمتر شده و نتیجه بهتری حاصل شود. در ایستگاه آبادان استفاده از مدل MLP چنین نتیجه‌ای را به دست می‌دهد. ضمن اینکه در ایستگاه اهواز مدل RBF بهترین مدل شناخته شد.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of Dust Storms in Khuzestan Province Using Artificial Neural Networks

نویسندگان [English]

  • Masoud Pourgholam-Amiji 1
  • Mohammad Ansari Ghojghar 1
  • Khaled Ahmadaali 2

1 Ph.D. Candidate, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 Assistant Professor, Department of Reclamation of Arid and Mountainous Regions, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

چکیده [English]

. In this study to predict dust storms, hourly dust data and monthly data maximum, minimum, average temperature, maximum wind speed, and total precipitation in three synoptic stations of Abadan, Ahvaz, and Bostan with statistics period for 25 years (1990-2014) were collected. To investigate the impact of dust storms from climatic fluctuations, in addition to the mentioned variables, the Standardized Precipitation Evapotranspiration Index (SPEI) was also calculated in the seasonal time window. Predicting the number of days with seasonal dust storms using four artificial intelligence methods including MLP, ANFIS, RBF, and GRNN was performed. These were evaluated in the form of three experiments including the effect of adding auxiliary features on the prediction, the effect of the number of previous seasons on the prediction, and the best technique among the models used. The results showed that in all stations, the use of all features has improved dust prediction and the value of the Mean Absolute Error (MAE) for Abadan, Ahvaz, and Bostan stations is equal to 1.15, 1.66, and 0.66, respectively were obtained, all of which were related to the autumn season. In conclusion, it can be said that in Bostan station, if all the features and data of the last four seasons are used, the ANFIS model as input causes the prediction error to be reduced and a better result to be obtained. In the Abadan station, using the MLP model gives such a result.

کلیدواژه‌ها [English]

  • climatic parameters
  • Neural Networks
  • Dust Storms
  • SPEI
  • Artificial intelligence
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