Application of machine learning in predicting sources of water pollution in the Euphrates and Tigris rivers in Iraq
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| 出版年: | International Journal of Aquatic Biology vol. 12, no. 6 (Dec 2024), p. 581 |
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| 第一著者: | |
| その他の著者: | , , , , |
| 出版事項: |
Iranian Society of Ichthyology
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.22034/ijab.v12i6.2421 |2 doi | |
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| 100 | 1 | |a Rashid, Mohammed Kareem |u Electronic Computing Center, University of Misan, Maysan, Iraq | |
| 245 | 1 | |a Application of machine learning in predicting sources of water pollution in the Euphrates and Tigris rivers in Iraq | |
| 260 | |b Iranian Society of Ichthyology |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a New evaluation and control methods are required to address the ecological, economic, and public health concerns raised by the contamination of the rivers Tigris and Euphrates. To minimize negative effects on ecosystems, our research built and implemented a machine learning framework to track down and foresee potential water contamination hotspots. To examine the causes of pollution and its consequences on aquatic ecosystems, researchers combined data from multiple sources, such as aerial photographs, field surveys, and official government documents. Predictive models encompass significant attributes such as pesticides, mineral composition, suspended particulates, diversity of macroinvertebrates, and habitat quality. Feature selection techniques, including LASSO regression and recursive feature elimination, ensured dependable model construction. Four machine learning algorithms of MCP, K-nearest neighbors, decision tree, and multi-layer perceptron were employed for pollution source recognition and impact prediction. The models correctly identified significant pollution sources, including untreated sewage, agricultural runoff, and industrial discharges. The concentration and distribution patterns of pollutants were elucidated by clustering and regression techniques. The results indicated reduced biodiversity, habitat degradation, and toxic algal blooms, as well as identified significant pollution areas. This research shows that machine learning can transform environmental monitoring and water resource management. The study's practical findings, which integrate ecological and computational methodologies, can assist policymakers and water resource managers. | |
| 653 | |a Pesticides | ||
| 653 | |a Agricultural runoff | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Algal blooms | ||
| 653 | |a Environmental degradation | ||
| 653 | |a Resource management | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Public health | ||
| 653 | |a Macroinvertebrates | ||
| 653 | |a Biodiversity | ||
| 653 | |a Rivers | ||
| 653 | |a Machine learning | ||
| 653 | |a Contamination | ||
| 653 | |a Particulates | ||
| 653 | |a Prediction models | ||
| 653 | |a Decision trees | ||
| 653 | |a Eutrophication | ||
| 653 | |a Sewage | ||
| 653 | |a Water quality | ||
| 653 | |a Water resources management | ||
| 653 | |a Environmental quality | ||
| 653 | |a Clustering | ||
| 653 | |a Aerial surveys | ||
| 653 | |a Algae | ||
| 653 | |a Algorithms | ||
| 653 | |a Water resources | ||
| 653 | |a Pollution sources | ||
| 653 | |a Pollution | ||
| 653 | |a Mineral composition | ||
| 653 | |a Aerial photographs | ||
| 653 | |a Ecosystems | ||
| 653 | |a Multilayers | ||
| 653 | |a Water pollution | ||
| 653 | |a Aquatic ecosystems | ||
| 653 | |a Aerial photography | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Suspended particulate matter | ||
| 653 | |a Control methods | ||
| 653 | |a Zoobenthos | ||
| 653 | |a Habitats | ||
| 653 | |a Impact prediction | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Salman, Israa Ramadhan |u College of Pharmacy, University of Misan, Maysan, Iraq | |
| 700 | 1 | |a Obaid, Abbas Luaibi |u College of Agriculture, University of Misan, Maysan, Iraq | |
| 700 | 1 | |a Hassan, Saif Al-Deen H |u Department Business Administrator, College of Administration and Economics, University of Misan, Maysan, Iraq | |
| 700 | 1 | |a Al-musawi, Mohammed Raoof |u SMonash University, Clayton 3168, Melbourne, Victoria, Australia | |
| 700 | 1 | |a Al-Saady, Moumal | |
| 773 | 0 | |t International Journal of Aquatic Biology |g vol. 12, no. 6 (Dec 2024), p. 581 | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3201565709/abstract/embedded/NVC8TPT9VN4WFQEG?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3201565709/fulltextPDF/embedded/NVC8TPT9VN4WFQEG?source=fedsrch |