•  
  •  
 

Document Type

Original Study

Keywords

Computer Engineering

Abstract

More than 66% of the Earth's total surface area is covered by water. Cleanwater is one of the basic needs of everyday life. Consistent pollution of water bodies canhave far-reaching effects on the lives of living organisms. The World Health Organizationhas reported that the provision of safe drinking water for human consumption is achallenge that has reached alarming levels. This is because nearly 70% of the total waterwithdrawals worldwide are used in agriculture. To determine the water's suitability forhuman consumption, Tests are typically conducted by examining the properties of water interms of physical, biological, and chemical conditions. There are various methods tomeasure water quality. Recently, an ongoing process has been shown to improve waterquality. To solve this problem, this paper is using a machine learning model to Contributionfor creation a smart model capable of classifying potable and non-potable water that isreleased to the water share by the competent authorities and the ability of MachineLearning models to monitor and predict water, especially Managing and Planning WaterResources. datasets were utilized in training and evaluating machine learning models,which includes (3276) samples with nine attributes and two labels indicating waterusability According to the in our work the (Random Forest) algorithm have the bestThrough the results that appeared by accuracy (0.954084, 0. 88 2484, and 0. 931849 forthree sizes of data.) and then (Decision Tree, K-Nearest Neighbor, Logistic Regression,SVC) sequentially

Download

Share

COinS