مدل‌سازی بارش- رواناب با استفاده از مدل HBV و الگوریتم جنگل تصادفی در حوضه آبخیز بازفت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی منابع آب، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران.

2 دانشیار گروه مهندسی آب؛ دانشکده کشاورزی؛ دانشگاه شهرکرد؛ شهرکرد؛ ایران

3 دکترای هیدرولوژی و منابع آب، گروه مهندسی آب، دانشکده علوم آب، دانشگاه شهید چمران اهواز، اهواز، ایران.

چکیده

برآورد رواناب حاصل از بارندگی در یک حوضه آبخیز از جهات گوناگون از جمله مدیریت مخازن سدها، مدیریت منابع آب، تنظیم سیلاب، کنترل فرسایش کناره و بستر رودخانه حائز اهمیت می­باشد. در این مطالعه، از مدل مفهومی HBV و مدل هوش مصنوعی جنگل تصادفی (RF) به منظور شبیه­سازی فرایند بارش-­رواناب در حوضه آبخیز بازفت در ایستگاه هیدرومتری لندی برای دوره آماری 2010 تا 2017 استفاده شد. برای ارزیابی عملکرد مدل­ها، از آماره­های ضریب همبستگی (r)، ریشه میانگین مربعات خطا (RMSE)، معیار کارایی نش–­ساتکلیف (NS)، میانگین مطلق درصد خطا (MAPE) و میانگین قدرمطلق خطا (MAE) استفاده شد. مقایسه نتایج مدل مفهومی HBV و مدل RF نشان‌دهنده عملکرد بهتر مدل RF بود. بنابراین، مدل RF با مقادیر (m3/s 39/0RMSE=، 59/9MAPE=، 25/0MAE=، 95/0 r= و 82/0NS=) به عنوان مدل برتر انتخاب گردید و این مدل می­تواند برای کاربردهای آینده به عنوان یک گزینه جدید برای پیش­بینی رواناب در حوضه بازفت مورد استفاده قرار گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

Rainfall- Runoff Modeling Using HBV Model and Random Forest Algorithm in Bazoft Watershed

نویسندگان [English]

  • Fatemeh Sohrabi Geshnigani 1
  • Rasoul Mirabbasi Najafabadi 2
  • Mohammad reza Golabi 3
1 MSc Student of Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.
2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.
3 Ph.D of Hydrology and Water Resources, Department of Water Engineering, Faculty of Water Sciences, Shahid Chamran University of Ahvaz, Ahvaz, IRAN.
چکیده [English]

Estimation of runoff in a catchment area is important from various aspects such as dam reservoir management, water resources management, flood regulation, and erosion control in river banks and bed. In the present study, a conceptual model of HBV and an intelligent model of Random Forest (RF) were used to simulate the rainfall- runoff process in Bazoft watershed at the Landi hydrometric station during the period of 2010 to 2017. In order to evaluate the performance of models, the statistical criteria, including Correlation coefficient (r), Root Mean Squares Error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) were used. Comparing the results of HBV and RF models revealed that the RF model outperformed the HBV. Thus, the RF model with r=0.95, NS=0.82, MAPE=9.59, MAE=0.25, and RMSE=0.39 m3/s was selected as the top model which might be used as a new choice to predict runoff in Bazoft watershed.

کلیدواژه‌ها [English]

  • Rainfall
  • Runoff
  • Evapotranspiration
  • Random forest model
  • Bazoft watershed
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