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The Water Quality Index (WQI) is an effective water test that assesses water quality, identifies contaminants, and aids in decision-making.However, it is inefficient to analyze water samples in laboratories due to high costs, time-consuming processes, and limited ability to record temporal or geographical oscillations.Recently, the use of modern technologies such as Remote Sensing (RS) data, Geographic Information Systems (GIS), and Artificial Neural Networks (ANN), in combination with survey data, has confirmed an efficient tool to generate the WQI map of the Euphrates River in Ramadi, Iraq.In the present study, the RS data, such as Landsat 8 and Landsat 9 images, and laboratory tests of samples were used to develop a database for WQI based on spectral reflectance using the radial basis neural network model.The result of this model was then manipulated within ArcGIS 10.8 using the spatial analyst model to generate a digital map of WQI.This model was evaluated using seven criteria, which are correlation coefficient (r), mean absolute error, normalized mean absolute error, lowest absolute error, maximum absolute error, and root square equation of the coefficients (RMSE).The correlation value of the WQI was 0.93, which shows remarkable prediction accuracy.Therefore, this calculation method is effective in calculating the WQI and producing precise digital maps of water quality.
Jassam et al. (Thu,) studied this question.