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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

2021Environmental Science and Pollution Research47 citationsDOI

Topics & Concepts

OverfittingRandom forestMathematicsStatisticsGradient boostingAlgorithmBoosting (machine learning)Random variableArtificial intelligenceComputer scienceArtificial neural networkAir Quality Monitoring and ForecastingAir Quality and Health ImpactsUrban Heat Island Mitigation
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms | Litcius