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Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms |
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| Authors: | AlThuwaynee Omar F Kim Sang-Wan Najemaden Mohamed A Aydda Ali Balogun Abdul-Lateef Fayyadh Moatasem M Park Hyuck-Jin |
| Institution: | 1.Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-roGwangjin-gu, Seoul, 05006, Republic of Korea ;2.Ministry of Environment, Baghdad, Iraq ;3.Department of Geology, Faculty of Sciences, Ibn Zohr University, B.P. 8106, 80000, Agadir, Morocco ;4.Geospatial Analysis and Modelling (GAM) Research Laboratory, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Perak, Malaysia ;5.Engineering Services and Asset Management, John Holland Group, Sydney, NSW, 2150, Australia ; |
| Abstract: | Environmental Science and Pollution Research - This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the... |
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