Neural network modelling of Tds concentrations in Shatt Al-Arab River Water

Abstract EN

River water salinity is a big concern in many countries, considering agricultural and drinking usages.

Therefore, prediction of amount of Total Dissolved Solid (TDS) is a necessary tool for planning and management of water resources.

Shatt Al-Arab river basin in Basrah which is located in south of Iraq suffer from high salinity, therefore use of the water for irrigation and drinking has become problematic.

In this regard, prediction of future TDS of Shatt Al-Arab river basin was studied using Artificial Neural Network (ANN).

Data measured monthly from January 2007 up to December 2012 at monitoring station in the middle point along to the Shatt Al-Arab river has been used for training of the selected ANN.

Some of water quality parameters such as, power of hydrogen (pH), Total Hardness (TH), Magnesium hardness (MgSO4), Calcium hardness (CaSO4), Chlorides (Cl), Sulphates (SO4), turbidity (TU) and electrical conductivity (EC) were considered as inputs for the ANN and Total Dissolved Solid (TDS) was the output of the model.

The validation of the neural network model showed very good agreement for predictions of the TDS concentrations between observed and simulated values.

The coefficient of correlation (R), during the validation process was found to be (1), and the mean squared error (MSE) was (0.075).

This work supports the concept that the neural network approach is a successful method of modelling such complex and nonlinear behavior of TDS in the rivers with different environmental conditions.