Credibility-driven identification of cropland runoff source in surface waters using ANN-XGBoost model ensemble powered by microbial fingerprints

•Five microbial fingerprints for cropland runoff source were identified.•ANN and XGBoost emerged as optimal classifiers using fingerprint data.•An ANN-XGBoost ensemble model significantly enhanced prediction performance.•A five-tier credibility classification system was developed for predictions. Hi...

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Veröffentlicht in:	Water research (Oxford) Jg. 288; H. Pt B; S. 124692
Hauptverfasser:	Li, Pengcheng, Dong, Lu, Li, Liping, Xue, Mengzhu, Xia, Guohui, Wang, Kening, Zhang, Xin, Liu, Peng, Zhang, Cheng, Cui, Baoshan, Bai, Junhong, Liu, Xinhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 01.01.2026
Schlagworte:	Agriculture Boosting Machine Learning Algorithms Cropland runoff source Environmental Monitoring - methods Machine Learning Machine learning classification models ensemble Microbial fingerprints Neural Networks, Computer Sewage Source identification Uncertainty analysis Wastewater - microbiology Water Microbiology Cropland runoff source Source identification Microbial fingerprints Machine learning classification models ensemble Uncertainty analysis
ISSN:	0043-1354, 1879-2448, 1879-2448
Online-Zugang:	Volltext
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