کاربرد مدل‌های فصلی سری‌ زمانی در پیش‌بینی جریان ماهانه ورودی به مخزن سدهای یامچی و سبلان در حوضه آبخیز قره‌سو، اردبیل

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران

2 گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی

چکیده

پیش­بینی حجم آب ذخیره شده در سدهای مخزنی در دوره­های آتی، نقش مهمی در برنامه­ریزی و مدیریت بهره­برداری بهینه از سامانه­های منابع آب دارد. در این مطالعه، از روش تحلیل سری­های زمانی برای پیش­بینی جریان ماهانه ورودی به سد­های مخزنی یامچی و سبلان در استان اردبیل استفاده شد. داده­های دبی جریان ماهانه اندازه­گیری شده در ایستگاه­های هیدرومتری واقع در محل ورود آب به سد، طی سال­های 94-1373 به مدت 21 سال تهیه و برای ساخت و آزمون مدل مناسب، به کار برده شد. پس از ایستا نمودن سری داده­ها، با توجه به نمودارهای خودهمبسته (ACF) و خودهمبسته جزئی (PACF)، ساختارهای مدل فصلی تشخیص داده شدند و پس از مقایسه آن­ها با توجه به معیارهای آکائیکه (AIC)، آکائیکه اصلاح شده (AICC) و اطلاعات بیزی (BIC)، مدل مناسب برای هر یک از ایستگاه­های هیدرومتری انتخاب شد. با برازش مدل به داده­های مشاهداتی، پارامترهای هر مدل تعیین و کفایت مدل­های منتخب نیز با آزمون­های تشخیصی بررسی گردید. نتایج نشان داد مدل ARIMA(1,0,0)(0,1,1)12 و ARIMA(1,1,1)(0,1,1)12 به­ترتیب برای داده­های دبی ماهانه ایستگاه یامچی و ارباب­کندی، دارای کمترین مقدار شاخص­های آماری ریشه میانگین مربعات خطا (RMSE) و میانگین قدرمطلق خطا (MAE) بوده و دارای بیش­ترین ضریب تعیین است. مقدار این شاخص­ها در مدل مربوط به ایستگاه هیدرومتری یامچی به­ترتیب برابر 04/1، 606/0 و 63/0 و برای ایستگاه هیدرومتری ارباب­کندی به ترتیب برابر 35/1، 8/0 و 74/0 به­دست آمد. ­لذا مدل­های منتخب، جریان ماهانه ورودی به مخزن سد­های یامچی و سبلان را با دقت خوبی پیش­بینی می­کند. همچنین مقایسه نتایج پیش­بینی شده با داده­های مشاهداتی نشان داد در پیش­بینی مقادیر حد بالای دبی، مدل­های منتخب از دقت بالایی برخوردار نیستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of Seasonal Time Series Models for Prediction of Monthly Inflow to Yamchi and Sabalan Reservoirs in Qarasu Catchment, Ardabil

نویسندگان [English]

  • Amin Kanooni 1
  • Soheila Urji 2
1 Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili
2 Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili
چکیده [English]

Predicting volume of water stored in reservoirs in the future periods plays an important role in planning and managing the optimal use of water resources systems. In this study, time series analysis method was used to predict the monthly inflow to Yamchi and Sabalan reservoirs in Ardabil province. The monthly flow data measured at hydrometric stations, located at the dam's entrance for 21 years (1994 to 2015) were used to build and test an appropriate model. The structures of the seasonal models were identified according to the auto-correlation charts (ACF) and partial auto-correlation (PACF), and then the appropriate model for each hydrometric station was selected based on the Akaike Information Criterion (AIC), Akaike Information Criterion Correction (AICC) and Bayesian Information Criterion (BIC). By fiting the model to the observational data, the parameters of each model were determined and the adequacy of the selected models was also examined by diagnostic tests. The results showed that ARIMA (1,0,0)(0,1,1)12 and ARIMA (1,1,1)(0,1,1)12 models, respectively for the monthly flow data of Yamchi and Arbabkandi stations have the lowest root mean square error (RMSE) and mean absolute error (MAE) and the highest determination coefficient. The values of these indicators in the model related to Yamchi hydrometric station were 1.04, 0.606 and 0.63, respectively, and for Arbabkandi hydrometric station were 1.35, 0.8 and 0.74, respectively. Therefore, the selected models accurately predict the monthly inflows to Yamchi and Sabalan reservoirs. Comparing the predicted results with the observational data showed that the selected models are not very accurate in predicting high discharge values.

کلیدواژه‌ها [English]

  • forecasting
  • Monthly Inflow
  • Reservoir operation
  • Seasonal model
  • time series
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