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The primary challenges addressed are the limited generalization abilities of models and the absence of a method to resolve conflicts among multiple objectives. The proposed framework integrates diverse learners, including support vector regression, random forest, extreme gradient boosting, and multi\u2010layer perceptron. It utilizes a dynamic weight allocation matrix along with a hybrid meta\u2010learning strategy to achieve simultaneous prediction and collaborative optimization of the removal rates of multiple pollutants. Furthermore, this paper introduces an innovative, improved whale\u2010grey wolf hybrid optimization algorithm (IWOAGWO). This algorithm incorporates adaptive inertia weights and an asymmetric convergence factor, which effectively balances global exploration with local exploitation. It optimally adjusts the configurations of nine key hyperparameters in the model, addressing the oscillation problem that is often caused by traditional random parameterization. Experimental results demonstrate that the IWOAGWO\u2010AW\u2010Stacking model achieves an Index of Agreement (IA) of 0.865 for NH\n                    <jats:sub>4<\/jats:sub>\n                    \u2009\n                    <jats:sup>+<\/jats:sup>\n                    \u2009\u2010N, 0.838 for NO\n                    <jats:sub>2<\/jats:sub>\n                    <jats:sup>\u2212<\/jats:sup>\n                    \u2010N, and 0.822 for total phosphorus (TP), significantly outperforming benchmark models such as random forest and multi\u2010layer perceptron. Compared to traditional stacking models, the new method shows average improvements in three core indicators: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and IA, ranging from 20.48% to 27.20%, 10.13% to 18.96%, and 14.92% to 35.18%, respectively. This proposed system extends the use of machine learning in managing water environments and offers a flexible, intelligent decision\u2010making tool to optimize parameters in artificial wetland systems. It has significant theoretical importance and potential for engineering applications in multi\u2010scale modeling and the optimization control of complex ecosystems.\n                  <\/jats:p>","DOI":"10.1002\/cpe.70564","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T02:49:28Z","timestamp":1770950968000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Prediction of Pollutant Removal Rate in\u00a0Constructed Wetlands Based on Machine Learning"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8526-0263","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electrical and Computer Science Jilin Jianzhu University  Changchun China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiubo","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Electrical and Computer Science Jilin Jianzhu University  Changchun China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University  Changchun China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"issue":"3","key":"e_1_2_12_2_1","first-page":"391","article-title":"Research and Application Progress of Artificial Wetlands in China and Suggestions for Future Development","volume":"36","author":"Zhu H.","year":"2022","journal-title":"NSFC"},{"key":"e_1_2_12_3_1","first-page":"31","article-title":"Characteristics and Control Measures of Agricultural Non\u2010Point Source Pollution in the Northeast Black Soil Region","volume":"2","author":"Yan B.","year":"2019","journal-title":"Environment and Sustainable Development"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.119128"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agee.2024.109401"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-70262-4"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/wer.1569"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2972564"},{"key":"e_1_2_12_9_1","first-page":"14789","volume-title":"Advances in Swarm Intelligence. 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