Assessment of dissolved oxygen in Shatt Al-Arab River by other quality parameters of water using Artificial Neural Networks


The Major sources of water are surface and subsurface. Surface water includes Rivers, Reservoirs, Creek, Streams, etc. This paper deals with using a neural network model to recognize dissolved oxygen in Shatt Al-Arab. Within the present study, Shatt Al-Arab River (Basrah-Iraq) is considered as the study area with monthly observed data from 2009-2014. Artificial Neural Network (ANN) has been applied to pattern the relations among eight (8) water quality parameters which are devoted for predicting one parameter (1) so that to decrease the load of long experimental procedure. Physical and chemical parameters that are inserted in the model are: pH, total dissolved solids, electrical conductivity, sulphate, phosphate, calcium, magnesium and nitrate. Dissolved oxygen (DO) is included in the output models. The three layered feed-forward model with back-propagation multi-layer perception (MLP) models architecture of 8-8-1 for DO. The artificial neural network has got training successfully and has been tested with 70% and 30% of the data groups. Statistical criteria of correlation coefficient (R2) and mean square error (MSE) are used to evaluate performance of the models. The correlation coefficients of the artificial neural network model for predicting DO have been 0.99354 and 0.98237, and mean square error for the model are 0.007698 and 0.00122 respectively. It can be concluding that these techniques provide similar accuracy in estimating DO concentration and predicting the dissolved oxygen (DO) in Shatt Al-Arab