Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms

Omar F. AlThuwaynee, Sang Wan Kim, Mohamed A. Najemaden, Ali Aydda, Abdul Lateef Balogun, Moatasem M. Fayyadh, Hyuck Jin Park

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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 μm 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.]

Original languageEnglish
Pages (from-to)43544-43566
Number of pages23
JournalEnvironmental Science and Pollution Research
Volume28
Issue number32
DOIs
StatePublished - Aug 2021

Keywords

  • Air quality modeling
  • Landsat 8 OLI/TIRS imagery
  • Petroleum cities
  • PM10
  • Spectral indices
  • Urban planning

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