Applications of artificial intelligence (AI) models have been massively explored for various engineering and sciences domains over the past two decades. Their capacity in modeling complex problems confirmed and motivated researchers to explore their merit in different disciplines. The use of two AI-models (probabilistic neural network and multilayer perceptron neural network) for the estimation of two different water quality indicators (namely dissolved oxygen (DO) and five days biochemical oxygen demand (BOD₅)) were reported in this study. The WQ parameters estimation based on four input modelling scenarios was adopted. Monthly water quality parameters data for the duration from January 2006 to December 2015 were used as the input data for the building of the prediction model. The proposed modelling was established utilizing many physical and chemical variables, such as turbidity, calcium (Ca), pH, temperature (T), total dissolved solids (TDS), Sulfate (SO₄), total suspended solids (TSS), and alkalinity as the input variables. The proposed models were evaluated for performance using different statistical metrics and the evaluation results showed that the performance of the proposed models in terms of the estimation accuracy increases with the addition of more input variables in some cases. The performances of PNN model were superior to MLPNN model with estimation both DO and BOD parameters. The study concluded that the PNN model is a good tool for estimating the WQ parameters. The optimal evaluation indicators for PNN in predicting BOD are (R² = 0.93, RMSE = 0.231 and MAE = 0.197). The best performance indicators for PNN in predicting Do are (R² = 0.94, RMSE = 0.222 and MAE = 0.175).

This study goals to assess the concentrations of specified Heavy Metals (HMs) and quality of taps drinking water of Ramadi city, western Iraq. Heavy Metal Pollution Indices like heavy metal pollution index (HMPI ), heavy metal evaluation index (HMEI) and contamination degree (CD) were applied to assess the supplied water. The average concentrations of Lead (Pb), Nickel (Ni), Chromium (Cr), Arsenic (As) and Cadmium (Cd) in most stations exceed the maximum admissible concentration, while Iron (Fe) in most of stations was within the maximum admissible concentration according to local and global guidelines. (HMPI ) values of most stations were exceed the maximum critical value of 100. (HMEI) values of most stations were exceed the value of 10 recommended for drinking water. (CD) values of most stations were exceed the value of 1 recommended for drinking water. The pollution origins were assessed using principal component analysis (PCA) and clustering analysis (CA). The results indicate that contamination comes from anthropogenic causes being the most common and lithogenic sources being the least common. The present concentration of (HMs) in taps water is causing health and environmental problems, water with high (HMs) concentrations would need to be treated before being supplied to consumers.

Scientist and researchers were recognized the important of water quality, Water resources are especially susceptible aquatic systems to many pollutants sources. Determination of water quality characteristics are very significance in term of the environmental considerations. This study aims to assess the water quality of the Euphrates river in the Fallujah Euphrates Reach (FER) by measuring the concentration of chemical and physical properties of water using multiple devices. CWQI and WAWQI were also used to assess the quality of water and indicate its suitability for drinking purposes, based on standard specifications from IQS, WHO, and USEPA as a criterion for comparison purposes. The findings show that with the use of CWQI, the quality of water was fair based on IQS, good based on WHO, and marginal based on USEPA, while the quality ranged between good, poor, very poor, and unsuitable for drinking purposes as a result of using WAWQI, with the exception of station 7, where the value of the WAWQI was greater than 100 mg/L, which indicates that the water is not suitable for drinking purposes as a result of the pollution of this station from a point source, which caused the high concentration of turbidity, sulfate, BOD5, and total dissolved solids, and decreased the DO concentration in this station compared to the rest of the stations, which were within the permissible.

For turbidity removal, most drinking water treatment plants are using coagulants due to the presence of suspended and colloidal materials at the coagulation and flocculation units. Aluminium and sulphates salts are the widely used coagulants, such as aluminium sulphate (Alum) and ferric chloride. However, several researches have linked Alzheimer's disease to the use of aluminium sulphate. Hence, scholars have conducted several researches on the possibility to reduce the amount of aluminium sulphate by using natural material/plants base as coagulant aids. In this study, Mallow's Leaves Extracts (MLE) and Carob's Pods Extracts (CPE) were used as an alternative coagulant aid. Couples of coagulation tests were implemented to find the optimal dosage of aluminium sulphates used as coagulants. The results displayed that the maximum turbidity removal efficiency by adding 100% of each coagulant, i.e. alum, MLE and CPE, were 61.16, 51.175 and 37.12%, respectively. In addition, the minimum residual turbidity and maximum turbidity removal efficiency were 4.56 NTU and 97.72% by adding 22.5 alum and 7.5 MLE presenting 30 mg/L dosing. Further, the minimum residual turbidity and maximum turbidity removal efficiency were 15.4 NTU and 92.3% by adding 22.5 alum and 7.5 CPE presenting 30 mg/L dosing.