TY - JOUR
T1 - Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis
AU - Sharafati, Ahmad
AU - Haji Seyed Asadollah, Seyed Babak
AU - Motta, Davide
AU - Yaseen, Zaher Mundher
PY - 2020/9/9
Y1 - 2020/9/9
N2 - Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.
AB - Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.
KW - suspended sediment load
KW - ensemble machine learning
KW - prediction
KW - uncertainty analysis
U2 - 10.1080/02626667.2020.1786571
DO - 10.1080/02626667.2020.1786571
M3 - Article
VL - 65
SP - 2022
EP - 2042
JO - Hydrological Sciences Journal
JF - Hydrological Sciences Journal
SN - 0262-6667
IS - 12
ER -