TY - JOUR
T1 - Ensemble machine learning paradigms in hydrology
T2 - A review
AU - Zounemat-Kermani, Mohammad
AU - Batelaan, Okke
AU - Fadaee, Marzieh
AU - Hinkelmann, Reinhard
PY - 2021/7
Y1 - 2021/7
N2 - Recently, there has been a notable tendency towards employing ensemble learning methodologies in assorted areas of engineering, such as hydrology, for simulation and prediction purposes. The diversity of ensemble techniques available for implementation in hydrological sciences has led to the development and utilization of different strategies in the implementation. This review paper explores and refers to the advancement of ensemble methods, including the resampling ensemble methods (e.g., bagging, boosting, and dagging), model averaging, and stacking viz. generalized stacked, in different application fields of hydrology. The main hydrological topics in this review study cover subjects such as surface hydrology, river water quality, rainfall-runoff, debris flow, river icing, sediment transport, groundwater, flooding, and drought modeling and forecasting. The general findings of this survey demonstrate the absolute superiority of using ensemble strategies over the regular (individual) model learning in hydrology. In addition, the boosting techniques (e.g., boosting, AdaBoost, and extreme gradient boosting) have been more frequent and successfully implemented in hydrological problems than the bagging, stacking, and dagging approaches.
AB - Recently, there has been a notable tendency towards employing ensemble learning methodologies in assorted areas of engineering, such as hydrology, for simulation and prediction purposes. The diversity of ensemble techniques available for implementation in hydrological sciences has led to the development and utilization of different strategies in the implementation. This review paper explores and refers to the advancement of ensemble methods, including the resampling ensemble methods (e.g., bagging, boosting, and dagging), model averaging, and stacking viz. generalized stacked, in different application fields of hydrology. The main hydrological topics in this review study cover subjects such as surface hydrology, river water quality, rainfall-runoff, debris flow, river icing, sediment transport, groundwater, flooding, and drought modeling and forecasting. The general findings of this survey demonstrate the absolute superiority of using ensemble strategies over the regular (individual) model learning in hydrology. In addition, the boosting techniques (e.g., boosting, AdaBoost, and extreme gradient boosting) have been more frequent and successfully implemented in hydrological problems than the bagging, stacking, and dagging approaches.
KW - Committee machine
KW - Data mining
KW - Hydroinformatics
KW - Random forest
KW - Soft computing
UR - http://www.scopus.com/inward/record.url?scp=85103939365&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2021.126266
DO - 10.1016/j.jhydrol.2021.126266
M3 - Review article
AN - SCOPUS:85103939365
SN - 0022-1694
VL - 598
JO - Journal of Hydrology
JF - Journal of Hydrology
M1 - 126266
ER -