برآورد ضریب رواناب رگبار با استفاده از هوش مصنوعی (مطالعۀ موردی: حوضۀ آبخیز کسیلیان)

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکتر‌ی واحد علوم تحقیقات دانشگاه آزاد اسلامی، تهران

2 استاد دانشگاه تربیت مدرس، تهران

3 استادیار واحد علوم تحقیقات دانشگاه آزاد اسلامی، تهران

4 دانشیار گروه مرتع و آبخیزداری دانشگاه تهران

5 استادیار واحد شوشتر دانشگاه آزاد اسلامی، شوشتر

چکیده

در تحقیق پیش رو تخمین ضریب رواناب با توجه به تأثیر پوشش گیاهی انجام ‏شده است. ابتدا مدل‏سازی ضریب رواناب با استفاده از داده‏های سیلاب و رگبار ساعتی طی دورۀ آماری 1366ـ 1388 انجام شده و ضرایب رواناب حوضۀ آبخیز کسیلیان تهیه شد. در مرحلۀ بعد، با استفاده از مدل‏های شبکۀ عصبی مصنوعی (ANN)، شبکۀ عصبی‌ـ فازی تطبیقی (ANFIS) و رگرسیون بردار پشتیبان (SVR) و عوامل مؤثر شامل شدت بارش، مقدار شاخص ، بارش 5 روز قبل و شاخص نرمال‌شدۀ اختلاف پوشش گیاهی (NDVI) ضریب رواناب در مقیاس رگبار برآورد شد. سپس، صحت و اهمیت هر یک از عوامل مؤثر بر ضریب رواناب حوضۀ آبخیز کسیلیان ارزیابی شد. نتایج نشان داد از بین سه مدل ANN، ANFIS و SVR، مدل ANN با مجذور میانگین مربعات خطا، ضریب تبیین، میانگین خطای اریبی و ضریب نش‌ـ ساتکلیف به‌ترتیب 08/0، 85/0، 84/0 و 01/0 در مرحلۀ آموزش و 12/0، 76/0، 74/0 و 03/0- در مرحلۀ آزمایش به عنوان مدل کارا در ارتباط با پیش‏بینی ضریب رواناب است. در مجموع، پیشنهاد می‏شود با توجه به اینکه ضریب رواناب کارکرد زیادی در فرایندهای هیدرولوژیک و بروز سیل دارد، بنابراین تخمین بهینۀ آن می‏تواند به مدیریت بهتر حفاظت آب و خاک و مدیریت فرسایش و رسوب حوضۀ آبخیز کمک کند.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of event based runoff coefficient using artificial intelligence models (Case study: Kasilian watershed)

نویسندگان [English]

  • Hossein Pourasadoullah 1
  • Mehxi Vafakhah 2
  • Baharak Motamedvaziri 3
  • Alireza Moghaddam Nia 4
  • Hossein Eslami 5
1 Science and Research Branch, Islamic Azad University, Tehran, Iran
2 خیابان حافظ-کوچه هاشمی نژاد شمالی-کوی گلشن ۴
3 Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, College of Agriculture & Natural Resources, University of Tehran, Daneshkadeh Ave., karaj, Iran
5 Faculty of Agriculture, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran
چکیده [English]

In this research, estimation of the Runoff Coefficient (RC) is carried out depending on land cover. Initially, RC modeling was performed using 54 hourly rainfall and corresponding runoff data during the period 1987–2010 in the Kasilian watershed. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) models and effective factors including rainfall intensity, Φ index (the average loss), five-day previous rainfall and Normalized Difference Vegetation Index (NDVI) were used to estimate RC. The results showed that the ANN model was more efficient than the other two models and had Mean Bias Error (MBE), Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (NSE) and Normalized Root Mean Square Error (NRMSE) equal to 0.08, 0.85, 0.84 and 0.37, respectively for the training phase and 0.12, 0.76, 0.74 and 0.47 for the test phase. In general, it is suggested that RC plays a major role in hydrological mechanisms and flooding. Thus, optimal estimation of RC can be helpful in better management of soil and water conservation and erosion and sediment management in this area.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • Normalized difference vegetation index
  • Runoff management
  • Soil and Water Conservation
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