[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122143-en":3,"doc-seo-122143-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122143,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Application of Machine Learning in Predicting Sources of Water Pollution in the Euphrates and Tigris","Contamination of the Tigris and Euphrates rivers raises ecological, economic, and public health risks that require improved evaluation and control. A machine learning framework was developed to identify and forecast likely water pollution hotspots. Multi-source datasets combined aerial imagery, field surveys, and government records, while models used variables such as pesticides, mineral composition, suspended particulates, macroinvertebrate diversity, and habitat quality. Feature selection (LASSO and RFE) supported reliable modeling, enabling recognition of key pollution sources and clarifying ecological impacts including biodiversity loss and toxic algal blooms.","Original Article  \nAbstract: 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.  \nArticle history:  \nReceived 17 October 2024  \nAccepted 22 December 2024  \nAvailable online 25 December 2024  \nKeywords:  \nAquatic Ecology Predictive modeling Classification algorithms Pollutant sources  \nIntroduction  \nWater pollution in the Euphrates and Tigris Rivers in Iraq threatens the integrity of these vital ecosystems, presenting a significant and escalating issue. The Euphrates and Tigris rivers support diverse freshwater ecosystems, including lotic and lentic habitats, riparian zones, and wetlands (Evans, 1994; Al-Ansari, 2021a; Fadhil et al., 2024) . These ecosystems are crucial in regional biodiversity and support over 52% of the Iraqi population. However, water quality has deteriorated due to industrial discharges, untreated sewage, and agricultural runoff, negatively impacting the complex biological communities inhabiting these rivers (Al-Ansari, 2021a) . Drought conditions and  \nrunoff from irrigation exacerbate pollution in these rivers. Migration due to conflict increases reliance on river water for sanitation, further degrading water quality and impacting ecological function. Analysis of surface water shows significant contamination levels with negative ecological effects. In addition, for economies, businesses, public health systems, and ecosystems to be viable over the long term, the freshwater resources of the Tigris and Euphrates rivers are essential (FAO, 2016) . Algal turbidity and agricultural pesticides are common pollutants that damage aquatic ecosystems and biodiversity (Anderson et al., 2002; World Health Organization, 2017; Al-Abboodi and Kareem, 2023) . UNEP (2023)  \n*Correspondence: Saif Al-Deen H. Hassan DOI: [https://doi.org/10.22034/ijab.v12i6.2421](https://doi.org/10.22034/ijab.v12i6.2421)[ ](https://doi.org/10.22034/ijab.v12i6.2421)E-mail: [saif_aldeen@uomisan.edu.iq](saif_aldeen@uomisan.edu.iq)  \nand Fadhil et al. (2024) indicate that hazardous substances substantially affect water quality and the complex biological processes, local economies, and human societies dependent on these resources.  \nA promising new avenue for environmental restoration has arisen from exploring innovative wastewater treatment methods, including diatomaceous earth and other natural resources, which are sustainable and ecologically ad","cbCaikTb4dDRFFRh","https://ap.wps.com/l/cbCaikTb4dDRFFRh","pdf",230645,1,9,"English","en",105,"# Introduction\n## Water pollution risks in the Euphrates and Tigris\n## Ecological and socio-economic relevance\n## Need for improved monitoring and restoration methods\n# Methods and Modeling Approach\n## Data sources and selected environmental attributes\n## Feature selection for dependable modeling\n## Machine learning algorithms for source recognition and impact prediction\n# Results and Implications\n## Identified pollution sources\n## Pollutant concentration and spatial patterns\n## Ecological consequences and management value","[{\"question\":\"Why are new evaluation and control methods needed for the Tigris and Euphrates rivers?\",\"answer\":\"Contamination threatens ecological integrity, harms biodiversity, and creates economic and public health concerns, requiring more effective monitoring and prediction approaches.\"},{\"question\":\"What kinds of data and variables were used to build the machine learning models?\",\"answer\":\"The study combined aerial photographs, field surveys, and official government documents, using attributes such as pesticides, mineral composition, suspended particulates, macroinvertebrate diversity, and habitat quality.\"},{\"question\":\"Which machine learning techniques were used to predict pollution sources and impacts?\",\"answer\":\"Four algorithms—MCP, k-nearest neighbors, decision tree, and multi-layer perceptron—were used for pollution source recognition and impact prediction.\"}]","Application of Machine Learning in 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are new evaluation and control methods needed for the Tigris and Euphrates rivers?","Question",{"text":75,"@type":76},"Contamination threatens ecological integrity, harms biodiversity, and creates economic and public health concerns, requiring more effective monitoring and prediction approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of data and variables were used to build the machine learning models?",{"text":80,"@type":76},"The study combined aerial photographs, field surveys, and official government documents, using attributes such as pesticides, mineral composition, suspended particulates, macroinvertebrate diversity, and habitat quality.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning techniques were used to predict pollution sources and impacts?",{"text":84,"@type":76},"Four algorithms—MCP, k-nearest neighbors, decision tree, and multi-layer perceptron—were used for pollution source recognition and impact 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