Abstract
This study aims to investigate the relationship between the water quality index (WQI) for irrigation purposes and four independent climate variables. Our case study was conducted on the Euphrates River within Karbala city, Iraq over the period between 2008 to 2016. The Bhar-gava WQI was calculated using nine physicochemical parameters, the electrical conductivity (EC), total dissolved solids, turbidity, pH, and calcium, magnesium, sodium, chloride and sulfate levels. The Bhragava WQI classified the Euphrates river as generally "good". Artificial neural network (ANN) and non-linear regression models were developed and used to forecast the relationship between the WQI and four independent climate variables (temperature, relative humidity, and rainfall depth and sunshine duration). The non-linear regression model was adopted to predicate the WQI because the coefficient of determination and minimum error value were better than those obtained with the ANN model. The non-linear model matched the calculated Bhargava WQI values and recorded meteorological data with a coefficient of determination (R2) = 78.2 and standard error = 2.1.
Highlights
Water resources such as rivers, streams and groundwater have an important role in the existence of life because water is essential for human, industrial, agricultural and domestic activities; this, in turn, depends on the classification of water quality
This study aims to investigate the relationship between the water quality index (WQI) for irrigation purposes and four independent climate variables
The results indicate that the Artificial neural network (ANN) model can estimate the WQI with suitable accuracy without these two independent parameters
Summary
Water resources such as rivers, streams and groundwater have an important role in the existence of life because water is essential for human, industrial, agricultural and domestic activities; this, in turn, depends on the classification of water quality. Changes in the water temperature, precipitation patterns and evaporation as well as extreme conditions, such as heavy rainfall, drought and flooding, lead to seasonal and temporal changes in the water quality and can cause various types of water pollution, such as nutrients, sediments, dissolved organic carbon, pesticides, pathogens and salt [1]. Increasing air temperature leads to an increase in the water temperature and evaporation, which will affect the biological and chemical properties of water resources by changing the mixing patterns and increasing the thermal stability, which can result in lower oxygen concentrations and a higher release of phosphorus from sediments i.e. the problems of water pollution, will increase [2]. The low water flow reduces the dilution of contaminants and the pollution in the residual water sources is increased
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