پیش‌بینی جریان ورودی سد امیرکبیر با استفاده از الگوهای دورپیوند اقلیمی و مدل‌های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده فناوری کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، تهران، ایران.

2 گروه مهندسی آبیاری و آبادانی، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، ایران.

10.22059/jwim.2023.354198.1044

چکیده

تقاضا برای آب شیرین به‌طور فزاینده­‌ای در حال افزایش است، درحالی‌که منابع محدود آب، در معرض اضافه­ برداشت، آلودگی و تغییرات اقلیمی هستند که این موارد نیاز به بهبود مدیریت منابع آب را به‌منظور توزیع عادلانه و دست‌یابی به اهداف توسعه پایدار برجسته می‌کند. یک گزینه کم­ هزینه برای حمایت از استراتژی‌­های مدیریت بهتر آب، توسعه مدل­‌هایی با قابلیت پیش‌­بینی مقادیر آب دردسترس، به‌ویژه مقادیر مربوط به بارش و جریان رودخانه‌­هاست. تنوع اقلیمی و تغییرات آب و هوایی، یک فرض اساسی برای پیش­بینی‌­های هیدروکلیماتولوژیکی است. یکی از جنبه‌­های قابل ­توجه این موضوع، همبستگی بین پدیده­‌های بزرگ ­مقیاس جوی-اقیانوسی یا الگوهای دورپیوند با فرایندهای هیدرولوژیکی در مقیاس محلی است که این الگوها می‌­توانند بر جریان ورودی به سدها نیز اثرگذار باشند. در این مطالعه از سه مدل یادگیری ماشین شبکه عصبی مصنوعی، شبکه عصبی بیزین و سیستم استنتاج عصبی-فازی سازگار برای پیش­‌بینی جریان ورودی به سدها بهره گرفته شده­ است تا کارایی آن‌ها مورد ارزیابی قرار بگیرد. بدین منظور 12 سناریو متشکل از متغیرهای بارش، جریان ورودی به سد و نُه شاخص­ اقلیمی با تأخیر تا شش گام زمانی، طراحی شد تا تأثیر استفاده از الگوهای دورپیوند به‌عنوان متغیرهای پیش­‌بینی ­کننده جریان یک ماه بعد سد امیرکبیر، موردبررسی قرار بگیرد. تحلیل نتایج این پژوهش نشان داد که استفاده از شاخص Nino3.4 با یک­ گام زمانی تأخیر و هم‌چنین شاخص PDO با دو گام زمانی تأخیر، می‌­توانند باعث افزایش دقت مدل نسبت به سناریوهای که در آن‌ها تنها از متغیرهای ایستگاهی استفاده شده ­است، شوند. طبق نتایج، شاخص Nino3.4 مؤثرترین شاخص بر جریان ورودی به سد امیرکبیر شناخته ­شد و سناریویی که در آن از شاخص نام­برده به همراه داده­‌های بارش و جریان یک و دوماه قبل به‌عنوان ورودی استفاده شده بود، در هر سه مدل، بالاترین دقت را به ثبت رساند. هم‌چنین عملکرد مدل ANFIS برای سناریوی نام­برده (سناریوی 9)، با مقادیر RMSE و R2، به‌ترتیب معادل با 69/5 مترمکعب بر ثانیه و 79/0، نسبت به دو مدل ANN و BNN بهتر بود، به ‌طوری‌که مقدار شاخص R2 برای بهترین سناریوی متشکل از متغیرهای ایستگاهی (سناریوی 5)، به میزان 15/0 افزایش یافته و مقدار شاخص RMSE نیز به میزان 78/0 مترمکعب کاهش یافته­ است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Amirkabir Dam Inflow Prediction Using Teleconnection Patterns and Machine Learning Models

نویسندگان [English]

  • Ehsan Vasheghani 1
  • Ali Massah Bavani 1
  • Abbas Roozbahani 1
  • Farhad Behzadi 1
  • Misagh Bidabadi 2
1 Department of Water Engineering, Faculty of Agricultural Technology, University College of Agriculture & Natural Resources, University of Tehran, Tehran, Iran.
2 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture, University College of Agriculture & Natural Resources, University of Tehran, Tehran, Iran.
چکیده [English]

The demand for freshwater is increasing, while the limited water resources are subject to over-harvesting, pollution, and climate change, which require improving water resource management to distribute it equitably and achieve It highlights the goals of sustainable development. A low-cost option to support better water management strategies is to develop models capable of predicting available water amounts, especially amounts related to precipitation and river flow. Climatic diversity and climate changes are basic assumptions for hydro climatological predictions. One of the remarkable aspects of this issue is the correlation between large-scale atmospheric-oceanic phenomena or Teleconnection patterns with hydrological processes on a local scale, and these patterns can also affect the inflow to the dams. This study uses three machine learning models, an artificial neural network, a Bayesian neural network, and an adaptive neuro-fuzzy inference system to predict dam inflow and evaluate their efficiency. For this purpose, 12 scenarios consisting of rainfall variables, inflow to the dam, and nine climatic indicators with a delay of up to six-time steps were designed to investigate the effect of using long-term models as predictive variables of the flow one month later in Amirkabir Dam. to be placed The analysis of the results of this research showed that the use of the Nino3.4 index with one-time step delay as well as the PDO index with two-time step delays can increase the accuracy of the model compared to the scenarios in which only station variables are used. to be According to the results, the Nino 3.4 index was found to be the most effective index on the inflow to Amirkabir Dam, and the scenario in which the mentioned index along with the rainfall and flow data of one and two months before was used as input, in all three The model recorded the highest accuracy. Also, the performance of the ANFIS model for the mentioned scenario (scenario 9), with RMSE and R2 values, equal to 5.69 and 0.79 cubic meters per second, respectively, was better than the ANN and BNN models, so the value of the R2 index for the best scenario consisting of station variables (scenario 5), it increased by 0.15 and the value of RMSE index decreased by 0.78 cubic meters.

کلیدواژه‌ها [English]

  • ANFIS
  • ANN
  • BNN
  • Inflow Prediction
  • Teleconnection Patterns

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مدیریت آب و آبیاری
دوره 13، شماره 2
تیر 1402
صفحه 451-469

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