با همکاری انجمن آبخیزداری ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشکده کشاورزی، دانشگاه تبریز

2 کارشناس ارشد، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد اهر

3 استادیار، دانشکده کشاورزی، دانشگاه شهرکرد

چکیده

سیل یکی از حوادث طبیعی است که هر ساله خسارات بسیاری در نقاط مختلف جهان به‌وجود می­‌آورد. پیش‌بینی دقیق سیلاب در کاهش خسارات جانی و مالی و مدیریت منابع آب از اهمیت بسزایی برخوردار است. هدف از مطالعه حاضر، مقایسه قابلیت­‌های روش­‌های رگرسیون ماشین بردار پشتیبان، مدل درختی M5 و مدل رگرسیون خطی در برآورد دبی سیلاب یک و دو ساعت آینده ایستگاه تازه‌­کند در رودخانه اهرچای می‌باشد. داده‌های تاریخی دبی-اشل ساعتی ایستگاه تازه­‌کند و 14 رویداد مهم سیل برای ایجاد مدل مورد استفاده قرار گرفت. نتایج نشان داد که روش رگرسیون ماشین‌ بردار پشتیبان با ضریب تبیین 0.96 و جذر میانگین مربعات خطا M3s-1) 0.0472) برای سیلاب یک ساعت بعد و 0.90=R2 و M3.s-1) RMSE=0.1596 برای سیلاب دو ساعت بعد بهترین نتیجه را ارائه نمود. گرچه مدل درختی M5 دقت نسبتا کمتری نسبت به روش رگرسیون ماشین بردار پشتیبان داشت، ولی به لحاظ ارائه روابط خطی ساده و قابل فهم می‌­تواند به‌عنوان یک روش کاربردی در پیش‌بینی دبی سیلاب­‌های ساعتی مورد استفاده قرار گیرد.

کلیدواژه‌ها

عنوان مقاله [English]

Technical Note: Hourly river flow forecast of Aharchay River using‏ ‏machine learning ‎methods

نویسندگان [English]

  • Mohammadtaghi Sattari 1
  • Mohammadreza Abdollah Pourazad 2
  • Rasoul Mirabbasi Najafabadi 3

1 Assistant Professor, Faculty of Agriculture‎, University of Tabriz, Iran‎

2 MSc, Faculty of Technology and Engineering, Islamic Azad University of Ahar, Iran

3 Assistant Professor, Faculty of Agriculture, Shahrekord University, Iran

چکیده [English]

Floods are the main natural disasters that produce serious agricultural, environmental, and socioeconomical damages in many parts of the world. Accurate estimation of river flow in streams can have a significant role in water resources management and in protection from possible damages. This study aims to compare the abilities of Support Vector Machine (SVM), M5 model trees and Linear Regression (LR) methods in forecasting hourly discharge flow of Aharchay River. The hourly water level-discharge and 14 flood events data of Aharchay River measured at the Tazekand hydrometric station was used for modeling. The results showed that the SVM method gives more accurate results than the M5 model and LR method in forecasting river flow for next one and two hours with the R2=0.96 and RMSE=0.0472 (m3s-1) and the R2=0.90 and RMSE=0.1596 (m3s-1), respectively. Comparing the performance of SVR and M5 models indicated that, however the SVR approach may present more accurate results than the M5 model tree, but the M5 model provides more understandable, applicable and simple linear relation in forecasting hourly discharge. Thus, the M5 model tree can be used as an alternative method in forecasting hourly discharge.

کلیدواژه‌ها [English]

  • Data Mining
  • Discharge
  • East Azerbaijan
  • M5 model trees
  • Support Vector Machine ‎‎(SVM)‎
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