Abstracted/indexed

A hybrid algorithm (GA-ANN) was used to predict the turbidity values in the drinking water plant in Al Qusayr. Several models built and fitness detected for each, the network with the highest fitness selected, then the network was constructed. The network was able to predict turbidity with high accuracy, R (0. 9940) and a RMSE of 0.1078. And 4 equations for determining the value of the residual aluminum was obtained using Gene expression, the best equation results were, RMSE = 0.02, R = 0.9.

Pipe burst in water distribution networks is an inevitable event. Pipe burst prediction helps to manage the maintenance of pipes, which reduce costs, water consumption and increase water network reliability. In this paper, we implement, compare and evaluate five artificial intelligence and machine learning methods for pipe failure prediction. Pipe failure data were collected during an eight-year-period in a real case study. Finally, the best method is selected based on some error criteria.

During this investigation of defluoridation of drinking water by modified natural zeolites by using cationic surfactants, the researcher used to evaluates the un modified natural zeolites treatment performance by surfactants modified natural zeolite. Hence the results of each item zeolites is described by graphs and table as discussed in manustricpit. Since the preparation of Surfactants modified natural zeolites treatments different factor have to be considered which may influences.

The performance analysis of water supply distribution for the study area is identifies the gap between water supply yearly or daily production can be fitting to water supply deficit due to pre- urbanization and design water supply project does not meet the current population growth rate. Unlike this in the distribution networking system same zone(2), obtains to drinking water supply intermittent supply due low pressure, low velocity and high water demands have to be designed to there.