@article{scholars14653,
             doi = {10.1007/s11356-021-13255-4},
           title = {Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms},
            year = {2021},
            note = {cited By 16},
       publisher = {Springer Science and Business Media Deutschland GmbH},
          number = {32},
         journal = {Environmental Science and Pollution Research},
          volume = {28},
           pages = {43544--43566},
          author = {AlThuwaynee, O. F. and Kim, S.-W. and Najemaden, M. A. and Aydda, A. and Balogun, A.-L. and Fayyadh, M. M. and Park, H.-J.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104056850&doi=10.1007\%2fs11356-021-13255-4&partnerID=40&md5=00a37e73e28bc9ef27fc2fbf0733d1dc},
        abstract = {This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 {\^I}1/4m diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC). Graphical abstract: Figure not available: see fulltext. {\^A}{\copyright} 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},
            issn = {09441344},
        keywords = {accuracy assessment; algorithm; gradient analysis; Landsat; machine learning; mapping method; particulate matter; satellite imagery; uncertainty analysis, algorithm; human; machine learning; particulate matter; uncertainty, Algorithms; Humans; Machine Learning; Particulate Matter; Uncertainty}
}