ارزیابی اثر متغیرهای تاثیرگذار بر پیش‌بینی سیلاب واریزه‌ای با استفاده از مدل شبکه بیزین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده پردیس ابوریحان-دانشگاه تهران-تهران-ایران

2 دانشیار دانشگاه تهران

3 گروه مهندسی آبیاری و زهکشی، دانشکده پردیس ابوریحان، دانشگاه تهران، تهران، ایران

4 دانشگاه تهران-پردیس ابوریحان، تهران، ایران

چکیده

 
پیش­بینی سیلاب واریزه­ای جهت کاهش خسارات ناشی از آن از اهمیت ویژه­ای برخوردار است. هدف این تحقیق پیش­بینی غلظت رسوبات سیلاب (واریزه­ای و معمولی) توسط مدل­های شبکه بیزین و شبکه عصبی در حوضه‌های امامه، ناورود و کسیلیان است که به ترتیب در استان­های تهران، گیلان و مازندران واقع شده­اند. بدین­منظور، متوسط ارتفاع، شیب حوضه، مساحت حوضه، بارش فعلی، بارش پیشین (به مدت 3 روز قبل) و دبی 1 روز قبل به عنوان متغیرهای ورودی انتخاب شدند. سپس برای تعیین مؤثرترین عوامل بر غلظت رسوبات سیلاب، 32 سناریو ارزیابی شد. برای سناریو حاصل از کلیه عوامل منتخب، شاخص­های R2 و MAPE در مرحله آزمون، به ترتیب 97/0 و %55/8 برآورد گردید. ارزیابی اثر متغیرهای مختلف نشان داد مؤثرترین عوامل بر دقت پیش­بینی شبکه بیزین به ترتیب ارتفاع حوضه، بارش فعلی، دبی روز قبل، مساحت حوضه و بارش پیشین یک روز قبل می­باشند. شاخص­های R2 و MAPE برای این سناریو 91/0 و %01/11 است که به دلیل داشتن کمترین تعداد عوامل ورودی و بالاترین دقت به عنوان بهترین سناریو انتخاب گردید. مقایسه عملکرد مدل بیزین با مدل شبکه عصبی نشان داد مدل شبکه بیزین دقت پیش­بینی بالاتری دارد. مؤثرترین عوامل شناسایی شده می­تواند برای پیش­بینی سیلاب واریزه­ای در حوضه­های مشابه استفاده گردد.

کلیدواژه‌ها


عنوان مقاله [English]

Assessment of Effective Factors on the Forecasting of Debris Floods Using Bayesian Network Model

نویسندگان [English]

  • Mahsa Sheikh Kazemi 1
  • Mohammad Ebrahim Banihabib 2
  • Jaber Soltani 3
  • Abbas Roozbahani 3
  • Mitra Tanhapour 4
1 College of Abouraihan, University of Tehran, Tehran, Iran
2 Associate professor, University of Tehran
3 Department of Irrigation and Drainage Engineering, College of Abouraihan, University of Tehran, Tehran, Iran
4 University College of Abouraihan, University of Tehran, Tehran, Iran.
چکیده [English]

It is important to predict debris flood for reducing its damages. The aim of this study is the prediction of sediment concentration of debris floods and ordinary floods using bayesian network (BN) and artificial neural network (ANN) models in Ammameh, Navrood and Casilian basins which were located in Tehran, Gilan and Mazandaran provinces, respectively. Accordingly, average basin elevation (EL), average basin slope (S), watershed area (A), current day rainfall (R), antecedent rainfall (AR) of three-days ago and discharge of one-day ago were selected as input variables. Then, 32 scenarios were tested to determine the most effective factors on the sediment concentration of flood. For the scenario derived from all selected factors, indices R2 and MAPE in the test stage were obtained 0.97 and 8.55%, respectively. Assessment of the effect of different factors shows that the most effective factors on the BN model’s prediction accuracy are EL, R, PQ, A and AR one-day ago. Indices R2 and MAPE for this scenario were obtained 0.916 and 11.01%, respectively. It was selected as the best scenario because the least number of predictors and the highest accuracy. The most effective factors identified in this study can be used to predict debris flood in similar basins.

کلیدواژه‌ها [English]

  • Debris flood
  • Sediment Concentration
  • Bayesian Network Model
  • Artificial Neural Network
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