Abstract
This study was aimed to investigate the development and evaluation of artificial intelligence techniques by using multilayer neural network. Levenberg–Marquardt back propagation (LMA) training algorithm was applied for calculating drinking water quality index (WQI) for Euphrates river (IRAQ). The transfer functions in the artificial network model were tangent sigmoid and linear for hidden and output layers, respectively. Eleven neurons presented for good prediction for results of (WQI) with a coefficient of correlation >0.97 and statistically calculated WQI values, inferring that the model predictions explain 94% of the variation in the calculated WQI scores. The WQI score of the Euphrates was 142 considered as poor. The analysis of sensitivity revealed that the total dissolved solids (TDS) is the highest effective variable with the relative importance of (26.3%), followed by electrical conductivity (EC) (23.1%), pH (17.3%), calcium (Ca) (0.149), chlorides (Cl) (11.2%), Hardness (5.7%), Temperature (1.3%), respectively. It can be concluded that the model presented in this study gives a useful alternate to WQI assessment, which use sub indices formulae.
Highlights
Studies on water quality of rivers are enormously important
Turkey is the source of the Euphrates river, it passes through Syria lands enters Iraq western borders in Al-Qaem, it passes through 1060 km of Iraq lands
The Artificial Neural Networks (ANNs) represent an intelligence system that is capable of classifying the water body through an innovative and attractive solution by connecting output variables to input ones in a unified system
Summary
Studies on water quality of rivers are enormously important. Especially when rivers are the essential supplies of water. Water quality index (WQI) can be considered as a single value combining a group of multiple constituents according to their concentrations [2]. They have been designed to translate the aptness of water in specific uses. The water quality of the Euphrates was assessed according to eight parameters, which were monitored monthly for the period (2005-2010) adopted from the Ministry of Water Resources (IRAQ) – Environmental Studies Center. 2011 [21] and water parameters rating scales were multiplied by their relative weights and aggregated by the arithmetic mean to get the overall water quality index (WQI) as in Equations 1, 2, 3:. Each layer comprises the number of neurons for the achievement of the required calculations
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