پیش‌بینی خشکسالی با استفاده از مدل ترکیبی GEP-GARCH(مطالعه موردی: ایستگاه سینوپتیک سلماس)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی آب، گروه مهندسی آب، دانشکده کشاورزی،دانشگاه ارومیه، ارومیه، ایران

2 استادیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران

3 دانشیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران

4 استادیار گروه مهندسی عمران، دانشکده مهندسی عمران، دانشگاه صنعتی ارومیه، ارومیه، ایران

چکیده

پیش­بینی خشکسالی نقش مهمی در طراحی سیستم­های سازگاری با خشکسالی و اجرای عملیات تسکین ایفا می­نماید. داده­های هیدرولوژیک به‌صورت ترکیبی از بخش قطعی و تصادفی می­باشند. با توجه به اینکه داده­های تولیدی مدل­های هوشمند به‌صورت قطعی می­باشند، استفاده از رویکردی جدید برای اعمال بخش تصادفی در پیش­بینی این داده­ها می­تواند قطعیت مدل را افزایش دهد. در این تحقیق با ترکیب مدل برنامه­ریزی بیان ژن (GEP) و مدل سری زمانی مبتنی بر واریانس ناهمسان شرطی (GARCH) سعی در ارائه مدلی ترکیبی برای پیش­بینی خشکسالی شده است. بدین منظور پیش­بینی خشکسالی در ایستگاه سلماس با استفاده از شاخص خشکسالی SPEI در گام­های زمانی مختلف در طی دوره آماری 35 ساله و با پنج مدل ورودی مختلف شامل مقادیر شاخص SPEI از یک تا پنج گام زمانی تأخیر، مورد بررسی قرار گرفت. نتایج نشان داد که روش GEP در مقیاس زمانی کوتاه­مدت شاخص SPEI از دقت مناسب برخوردار نمی­باشد و با افزایش مقیاس زمانی عملکرد مدل بهبود پیدا می­کند. نتایج مدل ترکیبی نشان داد که خطای مدل GEP در همه مقیاس­های زمانی کاهش پیدا می­کند و این بهبود عملکرد در مقیاس­های زمانی کوتاه­مدت ملموس­تر می­باشد به‌نحوی‌که ضریب همبستگی در مقیاس زمانی سه‌ماهه در مدل ساده GEP از 622/0 به 891/0 در مدل ترکیبی افزایش پیدا کرده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Drought Prediction Using GEP-GARCH Hybrid Model (Case Study: Salmas Synoptic Station)

نویسندگان [English]

  • Abbas Abbasi 1
  • Keivan Khalili 2
  • Javad Behmanesh 3
  • Akbar Shirzad 4
1 Ph.D students in Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran
2 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran
3 Associate professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran
4 Assistant Professor, Department of Civil Engineering, Faculty of Civil Engineering, Urmia University of Technology, Urmia, Iran
چکیده [English]

Drought prediction plays an important role in designing drought adaptation systems and implementation of relief operations. Hydrological data is a combination of a definite and random section. Given the fact that the production data of intelligent models are definite, application of a new approach, using the random part in predicting this data can increase the certainty of the model. In this research, it was attempted to provide a hybrid model for prediction of drought using a combination of the Gene Expression Programming model (GEP) and the Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) time series model. For this purpose, drought prediction in Salmas station using SPEI drought index at different time scales was investigated during 35 years statistical period and with 5 different input models. The results showed that the GEP method does not have the appropriate accuracy in short-term time scale of SPEI index and it will be improved with increasing time scale. The results of the hybrid model showed that the error of GEP model decreases in all time scales, and this performance improvement is more tangible in the short-time scales, so that the correlation coefficient in three-month time scale in the GEP model has increased from 0.622 to 0.891 in the hybrid model.

کلیدواژه‌ها [English]

  • Drought
  • Nonlinear Time Series
  • Intelligent Model
  • SPEI
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