Volume 7, Number 4 (1-2015)                   ijhe 2015, 7(4): 511-530 | Back to browse issues page


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Rajaee T, Rahimi Benmaran R, Jafari H. Prediction of quality parameters (NO3, DO) of Karaj River using ANN, MLR, and Denoising-based combined wavelet-neural network based on Models. ijhe. 2015; 7 (4) :511-530
URL: http://ijhe.tums.ac.ir/article-1-5264-en.html

1- Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran
2- Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran , r.rahimi_b@yahoo.com
Abstract:   (2380 Views)

Background & Objectives: The prediction and quality control of the Karaj River water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, performance of artificial neural network (ANN), combined wavelet-neural network (WANN), and multi linear regression (MLR) models were evaluated to predict next month nitrate and dissolved oxygen of “Pole Khab” station located in Karaj River. Materials and Methods: A statistical period of 11 years was used for the input of the models. In combined WANN model, the real monthly-observed time series of river discharge (Q) and the quality parameters (nitrate and dissolved oxygen) were analyzed using wavelet analyzer. Then, their completely effective time series were used as ANN input. In addition, the ability of all three models were investigated in order to predict the peak points of time-series that have great importance. The capability of the models was evaluated by coefficient of efficiency (E) and the root mean square error (RMSE). Results: The research findings indicated that the accuracy and the ability of hybrid model of wavelet neural network with the attitude of elimniations of time series noise had beeb better than the other two modes so that hybrid model of Wavelet artificial neural network wase able the improve the rate of RMSE for Nitrate ions in comparison with neural network and multiple linear regression models respectively, amounting to 35.6% and 75.92%, for Dissolved Oxygen ion as much as 40.57% and 60.13%. Conclusion: owing of the high capability wavelet neural network and the elimination of the time series noises in the prediction of quality parameters of river’s water, this model can be convenient and fast way to be proposed for management of water quality resources and assursnce from water quality monitoring results and reduction its costs.

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Type of Study: Research | Subject: WATER
Received: 2014/06/13 | Accepted: 2015/06/27 | Published: 2015/06/27

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