ارزیابی کارایی فرامدل‌های هیبریدی یادگیری ماشین و باکس جنکینز به‌منظور مدل‌سازی طوفان‌های گرد و غبار (مطالعه موردی: استان خوزستان)

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

چکیده

تأثیر پدیدة گردوغبار در ایران آن قدر وسیع است که بیش از نیمی از استان‌های کشور را به نحوی با مسائل و محدودیت-های این پدیدة طبیعی درگیر کرده است که، علاوه بر اثرهای زیست محیطی، موجب اختلال در اجرای طرح‌های توسعة پایدار ملی شده و تاکنون پیامدهای منفی زیادی به دنبال داشته و خواهد داشت. این پژوهش سعی بر ارائه نوعی مدل ترکیبی جدید با استفاده از فرامدل‌های هیبریدی هوش‌مصنوعی و همچنین فرامدل‌های هیبریدی باکس جنکینز جهت پیش‌بینی و مدل‌سازی شاخص FDSD (فراوانی روزهای همراه با طوفان‌های گردوغبار)، در هفت ایستگاه سینوپتیک استان خوزستان با طول دوره آماری 40 سال (2020-1981) داشته است. الگوریتم‌های هیبریدی پیش‌بینی به کار رفته در این پژوهش شامل W-ANFIS، AF-SVM، ARIMA-NARX، SARIMA-SETAR می‌باشند. نتایج پیش‌بینی نشان داد که کاهش عملکرد مدل‌های هیبریدی جهت پیش‌بینی شاخص FDSD با کاهش فراوانی روزهای همراه با طوفان‌های گرد و غبار رابطه مستقیمی دارد. به نحوی که ضریب همبستگی برای داده‌های آزمایشی در فرامدل‌های AF-SVM و W-ANFIS به‌ترتیب از مقادیر 991/0 و 985/0 به 985/0 و 958/0 و ضریب نش ساتکلیف نیز به‌ترتیب از 977/0 و 960/0 به 973/0 و 952/0 کاهش یافته است. همچنین ضریب RMSE به ترتیب از ایستگاه آبادان تا دزفول برای دو فرامدل ذکر شده از مقدار 135/0 و 151/0 به 140/0 و 179/0 و ضریب MAE نیز به ترتیب از مقدار 054/0 و 068/0 به 060/0 و 093/0 افزایش یافته است. ضریب همبستگی برای داده‌های آزمایشی در فرامدل-های باکس جنکینز SARIMA-SETAR و ARIMA-NARX نیز به‌ترتیب از مقادیر 967/0 و 951/0 به 958/0 و 941/0 و ضریب نش ساتکلیف نیز به‌ترتیب از 945/0 و 923/0 به 938/0 و 913/0 کاهش یافته است که نشان‌دهنده ضعیف شدن عملکرد فرامدل‌های هیبریدی با کاهش فراوانی طوفان‌های گرد و غبار در استان خوزستان می‌باشد. همچنین با برازش چهار فرامدل هیبریدی بر روی شاخص FDSD نشان داده شد که فرامدل هیبریدی AF-SVM نسبت به سایر روش‌ها از عملکرد بهتری برخوردار بود. به نحوی که در همه ایستگاه‌های مورد مطالعه دارای ضریب همبستگی و نش ساتکلیف بیشتر و ضریب ریشه میانگین مربعات خطا و میانگین قدرمطلق خطا کمتری می‌باشد که نشان‌دهنده برتری این فرامدل هیبریدی نسبت به سایر فرامدل‌ها برای پیش‌بینی شاخص FDSD در استان خوزستان می‌باشد. نتایج این مطالعه می‌تواند جهت مدل‌سازی طوفان‌های گرد و غبار در سایر مناطق کشور نیز مورد استفاده قرار گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluating the Efficiency of Hybrid Metamodels of Machine Learning and Box Jenkins in Order to Model Dust Storms (Case Study: Khuzestan Province)

نویسندگان [English]

  • Mohammad Ansari ghojghar
  • javad bazrafshan
  • Shahab Araghinejad
Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

The impact of dust phenomenon in Iran is so vast that it has involved more than half of the country's provinces in some way with the issues and limitations of this natural phenomenon. In addition to the environmental effects, it has disrupted the implementation of national sustainable development plans and so far, it has had many negative consequences. This research tries to present a new hybrid model using artificial intelligence hybrid metamodels as well as Box Jenkins hybrid metamodels to predict and model the FDSD index (frequency of days with dust storms), in seven synoptic stations of Khuzestan province with length The statistical period has been 40 years (1981-2020). The hybrid prediction algorithms used in this research include W-ANFIS, AF-SVM, ARIMA-NARX and SARIMA-SETAR. The prediction results showed that the decrease in the performance of hybrid models to predict the FDSD index has a direct relationship with the decrease in the frequency of days with dust storms. So that the correlation coefficient for experimental data in AF-SVM and W-ANFIS hypermodels from 0.991 and 0.985 to 0.985 and 0.958, respectively, and Nash Sutcliffe coefficient has also decreased from 0.977 and 0.960 to 0.973 and 0.952, respectively. Also, the RMSE coefficient from Abadan station to Dezful for the two metamodels from 0.135 and 0.151 to 0.140 and 0.179 respectively, And the MAE coefficient has also increased from 0.054 and 0.068 to 0.060 and 0.093, respectively. Correlation coefficient for test data in Box Jenkins SARIMA-SETAR and ARIMA-NARX hypermodels also from 0.967 and 0.951 to 0.958 and 0.941 respectively and the Nash Sutcliffe coefficient has also decreased from 0.945 and 0.923 to 0.938 and 0.913, respectively, which indicates the weakening of the performance of hybrid metamodels with the decrease in the frequency of dust storms in Khuzestan province. Also, by fitting four hybrid hypermodels on the FDSD index, it was shown that AF-SVM hybrid hypermodel had better performance than other methods. In a way, in all studied stations, the correlation coefficient and Nash-Sutcliffe coefficient are higher and the root mean square error coefficient and the mean absolute value of the error are lower, which shows the superiority of this hybrid meta-model over other meta-models for predicting the FDSD index in Khuzestan province. The results of this study can be used to model dust storms in other western regions of the country.

کلیدواژه‌ها [English]

  • Prediction
  • FDSD Index
  • AF-SVM
  • W-ANFIS
  • Box Jenkins hybrid algorithms
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