شبیه سازی خشک‌سالی با استفاده از دو مدل تلفیقی CEEMD-GPR و GPR-GARCH (مطالعه موردی: شمال‌غرب ایران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد گروه آب، دانشکده عمران، دانشگاه تبریز

2 دانشجوی دکتری مهندسی و مدیریت منابع آب، دانشکده عمران، دانشگاه تبریز.

چکیده

خشک­سالی یکی از مهم­ترین حوادث طبیعی تأثیر­گذار بر بخش کشاورزی و منابع آب می­باشد. پیش­­بینی آن نقش مهمی در برنامه­ریزی و مدیریت منابع آب دارد. در تحقیق حاضر، ﺑﺎ اﺳﺘﻔﺎده از داده­های سه ایستگاه سینوپتیک ارومیه، تبریز و اردبیلواقعدرشمال­غرب کشور طی دوره زمانی (2017-1978) به پیش­­بینی ﺧشک­سالی پرداخته شده است. برای این منظور، ابتدا ﺷﺎﺧﺺ بارندگی استاندارد (SPI) در مقیاس زمانی شش­ ماهه محاسبه گردید. سپس با استفاده از روش­های تلفیقی CEEMD-GPR و GPR-GARCH، خشک­سالی سه ایستگاه مزبور پیش­بینی شد. برای بررسی کارایی روش­های تلفیقی، مدل­های متفاوتی با در نظر گرفتن شاخص SPIدوره­های قبل و عناصر اقلیمی به­عنوان پارامترهای وروردی تعریف شد و نرخ تأثیر هر یک از این پارامترها مورد بررسی قرار گرفت. با توجه به نتایج محاسبه شاخص خشک­سالی SPI مشخص شد که سطوح مختلف خشک­سالی طی سال­های 1985-1983، 1991-1988، 2001-1995، 2010-2005، 2013-2011 و 2017 در طول دوره آماری در سه منطقه رخ داده است. نتایج حاصل از تحلیل مدل­های تعریف شده براساس شاخص SPIدوره­های قبل و عناصر اقلیمی، دقت بالای روش­های­ تلفیقی به­کار­رفته در تحقیق حاضر را در تخمین شاخص خشک­سالی به خوبی نشان داد. به­طوری­که در تمامی ایستگاه­ها، درصد خطا با استفاده از روش­های تلفیقی CEEMD-GPRو GPR-GARCHنسبت به روش GPR تقریبا به میزان 25 تا 40 درصد کاهش یافت. ملاحظه گردید که در پیش­بینی خشک­سالی، عناصر اقلیمی شامل میانگین دما و رطوبت نسبی ماهانه و هم­چنین شاخص SPI­ مربوط به ماه­های گذشته تأثیر­گذار می­باشند. نتایج تحلیل حساسیت نشان داد که SPIt-1تاثیرگذارترین پارامتر در مدل­سازی است.

کلیدواژه‌ها


عنوان مقاله [English]

Drought Simulation using Two CEEMD-GPR and GPR-GARCH Integrated Models (Case Study: Northwest of Iran)

نویسندگان [English]

  • Kiyoumars Roushangar 1
  • Roghayeh Ghasempour 2
1 Professor, Department of Civil Engineering, University of Tabriz, Iran.
2 PhD candidate, Department of Civil Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

Drought is one of the most important natural disasters affecting agriculture section and water resources. Droughts often occur in arid and semi-arid regions. Therefore, drought forecasting is necessary and plays an important role in the planning and management of water resources. So far, numerous drought prediction methods have been proposed in the literature, including time series models, regression models, probabilistic models, machine learning models, physical models, and a host of hybrid models. Although all of these methods have shown promising results in terms of improving accuracy of drought forecasts, the impact of climate change on droughts has highlighted the need for more advanced methods for predicting this event. Engle (1982)  proposed the ARCH model which can depict the variance of the time series and eliminate the heteroskedasticity caused by the constant time series variance. The GARCH model was further developed based on the ARCH model, the advantage of which is that it can use a simpler form to represent a high-order ARCH model. On the other hand, in recent years, the Meta model approaches have been applied in investigating the hydraulic and hydrologic complex phenomena. Hybrid models involving signal decomposition have also been found to be effective in improving prediction accuracy of time series prediction methods (Amirat et al., 2018). Complementary Ensemble Empirical Mode Decomposition analysis is one of the widely-used signal decomposition methods for hydrological time series prediction. Decomposition of time series reduces the difficulty of forecasting, thereby improving forecasting accuracy.
Due to the complexity of the drought phenomenon and the effect of various parameters on its prediction, in this study, the capability of GPR as a kernel-based approach and also integrated CEEMS-GPR and GPR-GARCH models were assessed for drought modeling based on six-month SPI index for the three cities of Tabriz, Urmia, and Ardabil in Iran during the period 1978-2017. In fact, this study attempts to create a novel method by combining the CEEMD and GARCH models with the GPR to enhance the estimation accuracy of the six- month SPI drought index.

کلیدواژه‌ها [English]

  • Drought
  • Empirical mode
  • GPR
  • Nonlinear time series
  • rainfall
  • SPI
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