تحلیل عدم قطعیت پارامترهای مدل SVM برای برآورد بار رسوبات معلق و بستر در ایستگاه سیرا کرج با روش شبیه‌سازی مونت کارلو

نوع مقاله : مقاله پژوهشی

نویسندگان

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

2 استاد گروه هیدرولوژی و منابع آب، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز، اهواز، ایران

3 گروه مهندسی منابع آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

چکیده

برآورد میزان رسوب حمل شده توسط جریان برای برنامه‌ریزی و ذخیره منابع آب مخازن سدها و تغییرات بستر رودخانه­ها، مدیریت آبخیز، حفاظت سواحل و محیط زیست حائز اهمیت است. انتقال رسوب در رودخانه یک پدیده ذاتا غیرقطعی و پیچیده می‌باشد. دانش ناکامل در مورد فرآیند‌ها و داده‌ها، عدم‌قطعیت در برآورد انتقال رسوب را ایجاد می‌کند. عدم‌قطعیت پارامترها، از جمله منابع اصلی عدم‌قطعیت در برآورد بار رسوبات معلق و بستر است. در این مقاله از روش شبیه­سازی مونت کارلو برای برآورد عدم‌قطعیت بار رسوبات معلق و بستر به­علت عدم‌قطعیت در پارامترهای مدل ماشین بردار پشتیبان (SVM) در حوضه سد کرج استفاده شده است. برای انتخاب متغیرهای ورودی موثر در مدل SVM برای برآورد بار رسوبات معلق و بستر، از الگوریتم PMI استفاده شد. نتایج به­کارگیری الگوریتم PMI نشان می­دهد که تنها متغیر موثر در برآورد بار رسوبات معلق و بستر، دبی جریان در زمان حال است. نتایج نشان می‌دهد که عدم‌قطعیت در برآورد بار رسوب معلق با مدل SVM برای داده­های آموزش، آزمون و کل داده­ها به­ترتیب برابر با 8/12، 17 و 5/13 درصد است. همچنین عدم‌قطعیت در برآورد بار رسوب بستر با مدل SVM برای داده­های آموزش، آزمون و کل داده­ها به­ترتیب برابر با 5/23، 8/36 و 2/27 درصد است. بنابراین عدم‌قطعیت در برآورد بار رسوب بستر با مدل SVM بیشتر از عدم‌قطعیت در برآورد بار رسوب معلق است. به­کارگیری روش­های بهینه­سازی می­تواند برای برآورد دقیق مقادیر پارامترها و کاهش عدم­قطعیت در برآورد بار رسوبات معلق و بستر مفید باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Uncertainty Analysis of SVM Model Parameters for Estimating Suspended and Bed Sediment Load at Sierra Station in Karaj by Monte-Carlo Simulation Method

نویسندگان [English]

  • Alireza Keihani 1
  • Ali Mohammad Akhondali 2
  • Hosein Fathian 3
1 Department of Hydrology and Water Resources, Collage of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Professor of Hydrology and Water Resources Engineering Department, Collage of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 Department of Water Resources Engineering,, Ahvaz Branch, Islamic Azad University,, Ahvaz, Iran.
چکیده [English]

Estimation of sediment transported by the streamflow is important for planning and storing water resources of dam reservoirs and river bed changes, watershed management, coastal protection and the environment. Sediment transport in the river is an inherently uncertain and complex phenomenon. Incomplete knowledge of processes and data create uncertainty in estimating sediment transport. Parameters uncertainty is one of the main sources of uncertainty in estimating the suspended and bed sediment load. In this paper, the Monte Carlo (MC) simulation method is used to estimate the uncertainty of suspended and bed sediment load due to uncertainty in the parameters of the support vector machine (SVM) model in the Karaj Dam Basin. The partial mutual information (PMI) algorithm was used to select the efficient input variables in the SVM model to estimate the suspended and bed sediment load. The results of using PMI algorithm show that the only efficient variable in estimating the suspended and bed sediment loads is the current stream discharge. The results show that the uncertainty in estimating the suspended sediment load with SVM model for training, test and total data is equal to 12.8%, 17% and 13.5%, respectively. Also, the uncertainty in estimating the bed sediment load with SVM model for training, test and total data is equal to 23.5%, 36.8% and 27.2%, respectively. Therefore, the uncertainty in estimating the bed sediment load with SVM model is more than the one in estimating the suspended sediment load. Therefore, the use of optimization methods can be useful for accurate estimation of parameter values and reducing uncertainty in estimating the suspended and bed sediment load.

کلیدواژه‌ها [English]

  • Parameter Uncertainty
  • SVM model
  • Suspended and bed sediment load
  • PMI Algorithm
  • Monte-Carlo
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