ارزیابی کارایی مدل هیبریدی GRU-LSTM در پیش بینی طوفان های گرد و غبار (مطالعه موردی: استان خوزستان)

نوع مقاله : مقاله پژوهشی

نویسندگان

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

2 دانشیار گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

3 دانشیار دانشکده مهندسی عمران، پردیس دانشکده های فنی، دانشگاه تهران، تهران، ایران.

4 دانشجوی دکتری علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه ازاد اسلامی واحد اهواز، ایران.

چکیده

درک صحیح از وقوع طوفان‌های گرد و غبار در هر منطقه و آگاهی از تغییرات زمانی- مکانی این پدیده به مدیریت و کاهش خسارت‌های ناشی از گرد و غبار کمک شایانی می‌کند. در سال‌های اخیر، توسعه فرامدل‌ها و ترکیب آن‌ها با الگوریتم‌های بهینه‌سازی به منظور مدل‌سازی و پیش‌بینی متغیرهای آب و هوایی، مورد توجه زیادی قرار گرفته‌ است. از این رو در مطالعه حاضر، نوعی رویکرد ترکیبی به منظور پیش‌بینی فراوانی روزهای همراه با طوفان گرد و غبار (FDSD) در مقیاس فصلی پیشنهاد شده که در آن از ترکیب شبکه‌های عصبی LSTM و GRU استفاده می‌شود. در این پژوهش، عملکرد مدل هیبریدی پیشنهادی با شبکه عصبی مبتنی بر توابع پایه شعاعی (RBF) و ماشین بردار پشتیبان (SVM) مورد مقایسه قرار گرفته است. بدین منظور، از داده‌های ساعتی گرد و غبار و کدهای سازمان جهانی هواشناسی در مقیاس فصلی با طول دوره آماری ۳۰ ساله (2019-1990) در هفت ایستگاه سینوپتیک استان خوزستان استفاده شد. نتایج معیارهای ارزیابی در مرحله آموزش و آزمایش مدل‌ها نشان داد که مدل هیبریدی GRU-LSTM عملکرد بهتری نسبت به سایر مدل‌های مورد استفاده به منظور پیش‌بینی فراوانی روزهای همراه با طوفان گرد و غبار ارائه می نماید؛ به طوری که مدل هیبریدی پیشنهادی با ضریب همبستگی (0/988-0/905=R)، ریشه میانگین مربعات خطا (RMSE=0/313-0/402 day)، میانگین قدر مطلق خطا (MAE= 0/144-0/226 day) و ضریب نش‌-ساتکلیف (0/903-0/819=NS)، عملکرد بهتری نسبت به سایر مدل‌های مورد استفاده در پیش‌بینی شاخص FDSD داشته است. در مجموع با مقایسه مدل‌های مورد استفاده، روش هیبریدی GRU-LSTM بهترین عملکرد و بعد از آن مدل SVM بهترین نتیجه را ارائه نمود. لذا مدل هیبریدی پیشنهادی می­تواند به عنوان ابزاری مناسب جهت پیش­‌بینی شاخص FDSD و به تبع آن اتخاذ تصمیمات مدیریتی به منظور کاهش خسارات طوفان­‌های گرد و غبار، در منطقه مطالعاتی مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluating the Performance of GRU-LSTM Hybrid Model in Predicting the Dust Storms Events (Case Study: Khuzestan Province in Southwest of Iran)

نویسندگان [English]

  • Mohammad Ansari Ghojghar 1
  • Shahab Araghinejad 2
  • Javad Bazrafshan 2
  • Banafsheh Zahraie 3
  • Ehsan Parsi 4
1 Ph.D. Candidate, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 Associate Professor, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
3 Associate Professor, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
4 Ph.D. Candidate, Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Islamic Azad University Ahwaz, Ahwaz, Iran.
چکیده [English]

Understanding the frequency of dust storms in each area and being mindful of temporal-spatial variation of this event can help to monitor and reduce the damages induced by dust events. Due to the increasing development of metamodels and their combination with optimization algorithms used to model and predict hydrological variables, machine learning models due to high accuracy in forecasting, in the form of a black box, have received a lot of attention. Therefore, in the present study, a hybrid approach is proposed to predict the Frequency of Dust Storm Days (FDSD) on a seasonal scale, which uses a combination of Lang Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. In this study, the performance of the proposed hybrid model was compared with a neural network based on Radial Basis Functions (RBF) and Support Vector Machine (SVM). For this purpose, hourly dust data and codes of the World Meteorological Organization were used on a seasonal scale with a statistical period of 30 years (1990-2019) for seven synoptic stations in Khuzestan province. The results of the evaluation criteria in the training and testing Stages showed that the GRU-LSTM hybrid model offered better performance than other models used to predict the frequency of days with dust storms; The proposed hybrid model with correlation coefficient (R) of 0.905-0.988, Root Mean Square Error (RMSE) of 0.313-0.402 day, Mean Absolute Error (MAE) of 0.144-0.236 day, and Nash-Sutcliffe (NS) of 0.819-0.903 had better performance compared to the other models used in predicting the FDSD index. In general, comparing the models used in this study, the GRU-LSTM hybrid method and the SVM model, respectively, provided the best prediction skills. As a result, application of the proposed hybrid model can be used as a suitable tool to predict the FDSD index and adopting management decisions to reduce the dust storms damages in the study area.

کلیدواژه‌ها [English]

  • prediction
  • Dust Storm
  • SVM
  • GRU-LSTM method
  • Khuzestan Province
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