ارزیابی مدلهای پیشرفته یادگیری ماشین در پیش‌بینی تراز سطح آب دریاچه ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت منابع آب، دانشکده فنی و مهندسی واحد اراک، دانشگاه آزاد اسالمی، اراک، ایران.

2 استادیار گروه عمران، دانشکده فنی و مهندسی واحد اراک، دانشگاه آزاد اسالمی، اراک، ایران.

چکیده

دریاچه‌ها نقش مهمی در چرخه هیدرولوژیکی دارند و پیش‌بینی سطح آب آنها می‌تواند اطلاعات حیاتی برای مدیریت آینده دریاچه­‌ها و اکوسیستم آنها فراهم کند. در پژوهش حاضر 2 مدل شامل پس هرس کردن درخت به روش کاهش خطای هرس و مدل ترکیبی REPT با مدل جنگل چرخان (ROF-REPT) توسعه و ساخته شد، و برای پیش‌بینی 1، 2 و 3 ماه آتی سطح آب دریاچه ارومیه در شمال غرب ایران مورد استفاده قرار گرفت. داده­‌های سری زمانی سطح آب از سال 2001 تا 2020 به دو دسته، به ترتیب برای ساخت مدل (از سال 2001 تا 2014) و اعتبارسنجی (از 2015 تا 2020) تقسیم شد. سناریوهای ورودی مختلف برای یافتن مؤثرترین سناریو ورودی از متغیرهای اقلیمی ساخته شد و مورد ارزیابی قرار گرفت. در نهایت مدل‌های توسعه‌یافته از طریق معیارهای بصری و کمّی ارزیابی شدند. نتایج نشان داد که مدل ترکیبی ROF-REPT دارای عملکرد بالاتری نسبت به مدل منفرد REPT برای تمامی 1، 2 و 3 ماه آینده است. ضریب نش (Nash-Sutcliffe Efficiency) برای مدل‌های منفرد بین 0/45 تا 0/87 و برای مدل‌های ترکیبی بین 0/53 تا 0/95 حاصل شد. همچنین، نشان داده شد که مدل‌های توسعه‌یافته قادر به پیش‌بینی سطح آب تا 3 ماه آینده هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluating the Performance of Advanced Machine Learning Models in Predicting the Urmia Lake Water Level

نویسندگان [English]

  • Rasul Hajian 1
  • Mohammad Reza Jalali 2
  • Reza Mastouri 2
1 PhD Candidate, Department of Civil Engineering, Arak branch, Islamic Azad University, Arak, Iran.
2 Assistant Professor, Department of Civil Engineering, Arak branch, Islamic Azad University, Arak, Iran.
چکیده [English]

Lakes play an important role in the hydrological cycle and predicting the water level in them can provide vital information for the future management of lakes and their ecosystems. To this aim, in this research two models were developed and run for 1-, 2- and 3-month water level forcasts for the Lake Urmia in north-west of Iran; the tree post-pruning using the REPT method and the combined REPT model with the ROF-REPT. The water level time series data from 2001 to 2020 were divided into two categories for model building (from 2001 to 2014) and for model validation (from 2015 to 2020). Different input scenarios were developed and evaluated to find the most effective input scenario of climate variables. Finally, the developed models were evaluated through visual and quantitative criteria. The results showed that the combined ROF-REPT model has a higher performance than the single REPT model for all forecasts of 1-, 2- and 3-months. Nash-Sutcliffe Efficiency was obtained between 0.45 and 0.87 for single models and between 0.53 and 0.95 for combined models. Also, it was shown that the developed models are able to predict the water level up to 3 months ahead.
 

کلیدواژه‌ها [English]

  • Water Level
  • Lake Urmia
  • Decision Tree Models
  • Hybrid Model
  • Iran
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