Performance Evaluation of Artificial Neural Networks for Predicting Rivers Water Quality Indices (BOD and DO) in Hamadan Morad Beik River

Document Type : Research Paper

Authors

Abstract

One of the important factor for development in each region is the availability of appropriate water
resources. In addition to water quantity quality is also of great importance. The aim of this study is
to medel the qualitative indices (BOD, DO) of river water using multi-layer perceptron neural
network. In this paper, the information and data from Morad Beik river of hamadan including 10
monthly parameters of water quality in a one-year period and at six stations were used to predict
biological exygen demand (BOD) and dissolved oxygen (DO), as indices affecting water quality.
Efficiency of the neural network model was evaluated by some statistical criteria including
correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE). In the
optimum structure of neural network the correlations coefficient for BOD and DO were 0.986 and
0.969, and root mean square errors were 8.42 and 0.84 respectively. The results indicated the ability
of multi-layers perceptron neural network as a suitable technique for simulating changes in BOD
and DO indices.

Keywords

Main Subjects


علیائی ا، قربانی م ع و جباری خامنه ح، 1387 . عملکرد حافظه اتورگرسیو و شبکههای عصبی مصنوعی در پیش بینی جریان روزانه رودخانه لیقوان. مجموعه مقالات سومین کنفرانس مدیریت منابع آب ایران (لوح فشرده). دانشکده مهندسی عمران دانشگاه تبریز. Bowers JA and Shedrow CB, 2000. Predicting stream water quality using artificial neural networks.WSRC-MS-2000-00112. Chen JC, Chang NB and ShiehWK, 2003.Assessing wastewater reclamation potential by neural network model. Engin Appl Artif Intell 166: 149–57. Hore A, Dutta S, Datta S and Bhattacharjee C, 2008. Application of an artificial neural network in wastewater quality monitoring: prediction of water quality index. International. Journal of Nuclear Desalination (IJND) 3: 160 - 74. Huiqun M and Ling L, 2008. Water quality assessment using artificial neural network. pp.13-5. International Conference on Computer Science and Software Engineering. Washington, DC, USA. Kunwar P, Singh AB, Amrita M and Gunja J, 2009. Artificial neural network modeling of the river water quality—A case study. Ecol Model 220:888–95. Kuo Y, Liu C and Lin KH, 2004. Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Res 38: 148–58. Kuo J, Hsieh M, Lung W and She N, 2007. Using artificial neural network for reservoir eutriphication prediction. EcolModel 200: 171–7. Kurunc A, Yurekli K and Cevik O, 2005. Performance of two stochastic approaches for forecasting water quality and stream flow data fromYesilirmak River. Turkey Environ Model Software. 20: 1195–200. May D and Sivakumar M, 2009. Prediction of urban storm water quality using artificial neural networks. Environ Model Software. 24: 296-302. Najah A, Elshafie A, Karim O and Jaffar O, 2009. Prediction of Johor river water quality parameters using artificial neural networks. Europ J Sci Res 28: 422-35.