Machine Learning-Based Contamination Detection in Water Distribution System
| dc.contributor.advisor | Getachew, Alemu (PhD) | |
| dc.contributor.author | Akalewold, Fikre | |
| dc.date.accessioned | 2022-06-24T06:06:52Z | |
| dc.date.accessioned | 2023-11-04T15:14:45Z | |
| dc.date.available | 2022-06-24T06:06:52Z | |
| dc.date.available | 2023-11-04T15:14:45Z | |
| dc.date.issued | 2020-06 | |
| dc.description.abstract | Water is a necessary component of all human activities. According to the United Nations World Water Assessment Program, every day, 2 million tons of sewage, manufacturing, and agricultural waste are discharged into the world's water. Due to population demands and dwindling clean water supplies as well as available water pollution management mechanisms, there is an urgent need to use computational methods to intelligently manage available water. To ensure the protection of drinking water, accurate detection of natural or deliberate pollution events in water delivery pipes is essential. Companies that have water must ensure that it is safe to drink. To resolve the global issue of rising water contamination, the design of water contamination detector models has monitored the security of water in pipelines when concentrations of water quality variables in the pipes surpass their maximum threshold is presented in this paper. This paper proposes artificial neural networks, specifically Convolutional Neural Networks, for automated water impurity, detection to refine the model must a picture of turbid water in the pipe is used to detect events. The algorithm of deep learning achieved 96.3 percent accuracy after extensive training with a dataset of 4220 images reflecting various levels of contamination. Besides that, the machine learning algorithm uses an efficient study of water turbidity and transparency levels to estimate the level of pollution in a specific sample of water. As the established model is combined with the current framework, it will provide a cost-effective way for the water company to obtain an estimate of water quality, alerting local and national governments to take action, and potentially saving millions of people throughout the world. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/32139 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Water Distribution System | en_US |
| dc.title | Machine Learning-Based Contamination Detection in Water Distribution System | en_US |
| dc.type | Thesis | en_US |