Using Neural Networks (Back Propagation Network) to Determine the Variables Affecting Water Pollution in the Euphrates River

Abstract

With the economy's steady growth and rapid development of modern industrial technology, environmental problems have increased. Water pollution is one of the complex problems that most countries of the world suffer from, especially developing countries, because it contains various pollutants, some of which can decompose as organic pollutants, and some of which are difficult to decompose, such as toxic heavy metals. In Iraq, according to the Iraqi Ministry of Environment - Water Pollution Division statistics for the year 2018, specifically the waters of the Euphrates River, a group of variables affecting the water quality of this river has been identified. The scarcity of water and the accumulation of various wastes in the water networks and on the banks of the Euphrates River also contributed greatly to the increase in the problem of pollution. The continued poor quality of the water has led to severe environmental health concerns. The research aims to shed light on the most important variables affecting the pollution of the waters of the Euphrates River and the water quality and quality in this river. The principal components analysis and the neural network (backpropagation algorithm) were used to determine the variables that affect the pollution of the Euphrates River water. They were applied to real data from the Ministry of Environment for the Euphrates water pollution. One of the most important conclusions we reached when using statistical methods was that the variable (DO2) is not significant and does not affect the pollution of the Euphrates River either. Still, When Using the algorithm, we get that all these variables (TH, TDS, EC) do not affect the water pollution of the Euphrates River.