مقایسه شبکه‌های عصبی نوع GMDHچند هدفی و شبکه خودباوری بیزین در پیش‌بینی کدورت آب تصفیه شده مطالعه موردی: تصفیه خانه بزرگ آب گیلان
Journal Title: آب و فاضلاب - Year 2016, Vol 27, Issue 2
Abstract
Comparison of Multi Objective GMDH-type Neural Network and Bayesian Belief Network in the Prediction of Treated Water Turbidity Case Study: Great Water Treatment Plant in Guilan Province Abstract In this paper, the factors affecting water turbidity removal are identified using the Response Surface Methodology (RSM). The GMDH-type Neural Networks and Bayesian Belief Network (BBN) are subsequently employed for modeling and predicting treated water turbidity using an input-output data set. To validate the proposed model, a case study is carried out based on 700 sets of data obtained from Guilan ¬WTP. For modeling, the experimental data obtained from the operation unit are divided into train and test sections (70% for training and 30% for testing). The predicted values are then compared with experimental ones. The determination coefficients of the predicted values for the two BBN algorithms, consisting of EM and GD, and the GMDH model are found to be 0.9388, 0.9196, and 0.97095, respectively. Evidently, the GMDH model outperforms the BBN model in predicting treated water turbidity.
Authors and Affiliations
Allahyar Daghbandan, Fereshteh Alitaleshi, Mehran Yaghoobi
Keywords
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