مقایسه مدل های ماشین بردار و شبکه عصبی تابع پایه شعاعی در پیش بینی کیفیت آب سیمینه رود

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران.

2 استادیار، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه کردستان، سنندج، ایران.

3 استاد، گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران.

چکیده

در این پژوهش عملکرد روش های ماشین بردار پشتیبان و شبکه عصبی تابع پایه شعاعی در پیش بینی کیفیت آب سیمینه رود مقایسه شده است. برای این منظور پارامترهای نسبت جذب سدیم و یون کلر به عنوان شاخص های کیفیت آب در مصارف کشاورزی در نظر گرفته شد. از داده های اندازه گیری شده یون سدیم، کلسیم، منیزیم، pH، EC و دبی جریان به عنوان ورودی مدل ها طی یک دوره آماری 12ساله (1393- 1382) در مقیاس ماهانه استفاده شد. ارزیابی نتایج بر اساس معیارهای ضریب همبستگی، جذر میانگین مربعات خطا و میانگین خطای مطلق انجام شد. نتایج دوره صحت سنجی در 4 ایستگاه پل-بوکان، داشبند بوکان، قزل‌گنبد و کاولان نشان داد که مدل ماشین بردار پشتیبان در مقایسه با شبکه عصبی تابع پایه شعاعی، دارای ضریب همبستگی بهتر(71.SVM: 0 تا 0.94، RBF: 0.3 تا 0.5)، ریشه میانگین مربعات خطای کمتر (SVM: 0.028 تا 0.075 mg/l، RBF: 0.0672 تا 0.317 mg/l)، خطای میانگین مطلق کمتر (SVM: 0.003 تا 0.033 و mg/l، RBF: 0.087 تا 0.19 mg/l) برای پارامتر یون کلر و با همان ترتیب مقادیر SVM: 0.63 تا 0.88 و RBF: 0.21 تا 0.38، SVM: 0.0013 تا 0.082 mg/l و RBF: 0.0147 تا 0/025 و mg/l، SVM: 0.0085 تا 0.046 mg/l و RBF: 0.0653 تا 0.0996 mg/l برای نسبت جذب سدیم است. لذا بر اساس نتایج مدل ماشین بردار پشتیبان نسبت به مدل شبکه عصبی تابع پایه شعاعی از دقت و عملکرد بهتری برای پیش بینی پارامترهای کیفیت آب رودخانه سیمینه رود برخوردار است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of the support vector machine and radial function neural network models in predicting of SiminehRood river water quality Iran.

نویسندگان [English]

  • Bahareh Hosseinpanahi 1
  • Saman Nikmehr 2
  • Kumars Ebrahimi 3
1 MSc student, Department of Irrigation & Reclamation Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
2 Assistant Professor, Department of water sciences and engineering, faculty of agriculture, University of Kurdistan, Sannandaj, Iran.
3 Professor, Department of Irrigation & Reclamation Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

In this study, the performance of Support Vector Machine (SVM) and Radial Base Neural Network approach in predicting the water quality of SiminehRood River was examined. For this purpose, the Sodium Adsorption Ratio (SAR) and Chlorine ions were considered as indicators of water quality in agricultural use. Sodium, calcium, magnesium, pH, Ec, and river flow rate were utilized as input monthly parameters throughout a 12-year period (2003-2014). The results evaluated based on correlation coefficient, root means square error and mean absolute error. The results of the validation period in 4 stations of Pol Bukan, Dashband Bukan, Ghezel Gonbad and Kaullan showed that the SVM model in comparison with the neural network of the radial base function, has higher correlation coefficient (SVM: 0.71 to 0.94, RBF: 0.3 to 0.5), the lowest root means square error (SVM: 0.028 to 0.075 mg/l, RBF: 0.0672 to 0.317 mg/l), a lower absolute mean error (SVM: 0.003 to 0.033 mg/l, RBF: 0.087 to 0.19 mg/l) for the chlorine ion parameter and in the same order SVM values: 0.63 to 0.88 and RBF: 0.21 to 0.38, SVM: 0.0013 to 0.0282 mg/l and RBF: 0.047 to 0.025 mg/l, SVM: 0.0085 to 0.046 mg/L and RBF: 0.0653 to 0.0996 mg/l for sodium absorption ratio.Therefore, the Support Vector Machine model has better accuracy and performance for predicting water quality parameters of SiminehRood River than the Radial Basis Function Network.

کلیدواژه‌ها [English]

  • Artificial intelligence
  • Modelling
  • Qualitative Parameters
  • Water resources
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