Wu, X.;                     Chen, Z.;                     Wang, B.;                     Luo, Y.;                     Du, A.;                     Wang, Q.;                     Bate, B.    
        Machine Learning-Based Spatiotemporal Acid Mine Drainage Prediction Using Geological, Climate History, and Associated Water Quality Parameters. Water 2025, 17, 2661.
    https://doi.org/10.3390/w17182661
    AMA Style
    
                                Wu X,                                 Chen Z,                                 Wang B,                                 Luo Y,                                 Du A,                                 Wang Q,                                 Bate B.        
                Machine Learning-Based Spatiotemporal Acid Mine Drainage Prediction Using Geological, Climate History, and Associated Water Quality Parameters. Water. 2025; 17(18):2661.
        https://doi.org/10.3390/w17182661
    
    Chicago/Turabian Style
    
                                Wu, Xinyu,                                 Zhitao Chen,                                 Bin Wang,                                 Yuanyuan Luo,                                 Aifang Du,                                 Qiong Wang,                                 and Bate Bate.        
                2025. "Machine Learning-Based Spatiotemporal Acid Mine Drainage Prediction Using Geological, Climate History, and Associated Water Quality Parameters" Water 17, no. 18: 2661.
        https://doi.org/10.3390/w17182661
    
    APA Style
    
                                Wu, X.,                                 Chen, Z.,                                 Wang, B.,                                 Luo, Y.,                                 Du, A.,                                 Wang, Q.,                                 & Bate, B.        
        
        (2025). Machine Learning-Based Spatiotemporal Acid Mine Drainage Prediction Using Geological, Climate History, and Associated Water Quality Parameters. Water, 17(18), 2661.
        https://doi.org/10.3390/w17182661