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Machine Learning Techniques for Monitoring Water Quality in Water Resources | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Volume 24, Issue 3, September 2024, Pages 16-30 PDF (1.4 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.24.3.2 | ||
| Authors | ||
| Hussam Hashim Hussein* ; Salam Muhsin Arnoos; Wissam Shaya Abbas | ||
| IT Department, Technical College of Management- Baghdad, Baghdad, Iraq | ||
| Abstract | ||
| More than 66% of the Earth's total surface area is covered by water. Clean water is one of the basic needs of everyday life. Consistent pollution of water bodies can have far-reaching effects on the lives of living organisms. The World Health Organization has reported that the provision of safe drinking water for human consumption is a challenge that has reached alarming levels. This is because nearly 70% of the total water withdrawals worldwide are used in agriculture. To determine the water's suitability for human consumption, Tests are typically conducted by examining the properties of water in terms of physical, biological, and chemical conditions. There are various methods to measure water quality. Recently, an ongoing process has been shown to improve water quality. To solve this problem, this paper is using a machine learning model to Contribution for creation a smart model capable of classifying potable and non-potable water that is released to the water share by the competent authorities and the ability of Machine Learning models to monitor and predict water, especially Managing and Planning Water Resources. datasets were utilized in training and evaluating machine learning models, which includes (3276) samples with nine attributes and two labels indicating water usability According to the in our work the (Random Forest) algorithm have the best Through the results that appeared by accuracy (0.954084, 0. 88 2484, and 0. 931849 for three sizes of data.) and then (Decision Tree, K-Nearest Neighbor, Logistic Regression, SVC) sequentially | ||
| Keywords | ||
| Machine Learning; potable water; Water Quality; water resources | ||
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