Corrigendum to "Direct and indirect simulating and projecting hydrological drought using a supervised machine learning method" [Sci. Total Environ. 898 (2023), 165523]
Sci Total Environ. 2024 Jun 20:930:172842.
doi: 10.1016/j.scitotenv.2024.172842.
Epub 2024 May 1.
1 Department of Hydrology, Meteorology and Water Management, Institute of Environmental Engineering, Warsaw University of Life Sciences, Warsaw, Poland; Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Telegraphenberg A 31, 14473 Potsdam, Germany. Electronic address: mohammad_eini@sggw.edu.pl.
2 College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
3 Department of Hydrology, Meteorology and Water Management, Institute of Environmental Engineering, Warsaw University of Life Sciences, Warsaw, Poland.