TY - JOUR
T1 - Suspended sediment load modeling using Hydro-Climate variables and Machine learning
AU - Aldin Shojaeezadeh, Shahab
AU - Al-Wardy, Malik
AU - Reza Nikoo, Mohammad
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/4/1
Y1 - 2024/4/1
N2 - Machine learning (ML) models have the potential to improve the accuracy of Suspended Sediment Load (SSL) predictions. However, their ability to incorporate climate data to enhance SSL predictions remains underexplored. This study investigates the efficacy of ML models in modeling sediment transport using hydro-climate variables as input data. Boosted decision tree models, including Xgboost, LightGBM, and Catboost, were employed to estimate SSL using hydro-climate variables such as streamflow and precipitation, as well as maximum and minimum temperatures. The models were trained, optimized, and evaluated using either climate/streamflow data or a combination of both, encompassing 47 gauges across the contiguous United States (CONUS) between 1950 and 2022. The results demonstrate that streamflow data alone is insufficient for accurate SSL estimation in all rivers. Consequently, integrating climate data and streamflow data proved effective in predicting SSL at the daily scale across diverse climate zones with commendable accuracy. The models achieved an average Nash-Sutcliffe Efficiency (NSE) of > 0.66, Kling-Gupta Efficiency (KGE) of > 0.67, and Willmott Index (WI) of > 0.83 during the unseen (test) phase. These findings highlight the potential of ML models, particularly when combining climate data with streamflow, to enhance SSL predictions and improve our understanding of sediment transport dynamics.
AB - Machine learning (ML) models have the potential to improve the accuracy of Suspended Sediment Load (SSL) predictions. However, their ability to incorporate climate data to enhance SSL predictions remains underexplored. This study investigates the efficacy of ML models in modeling sediment transport using hydro-climate variables as input data. Boosted decision tree models, including Xgboost, LightGBM, and Catboost, were employed to estimate SSL using hydro-climate variables such as streamflow and precipitation, as well as maximum and minimum temperatures. The models were trained, optimized, and evaluated using either climate/streamflow data or a combination of both, encompassing 47 gauges across the contiguous United States (CONUS) between 1950 and 2022. The results demonstrate that streamflow data alone is insufficient for accurate SSL estimation in all rivers. Consequently, integrating climate data and streamflow data proved effective in predicting SSL at the daily scale across diverse climate zones with commendable accuracy. The models achieved an average Nash-Sutcliffe Efficiency (NSE) of > 0.66, Kling-Gupta Efficiency (KGE) of > 0.67, and Willmott Index (WI) of > 0.83 during the unseen (test) phase. These findings highlight the potential of ML models, particularly when combining climate data with streamflow, to enhance SSL predictions and improve our understanding of sediment transport dynamics.
KW - Hydrogeomorphology
KW - Machine learning
KW - Sediment transport
KW - Sensitivity analysis
KW - Uncertainty analysis
UR - http://www.scopus.com/inward/record.url?scp=85186328421&partnerID=8YFLogxK
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UR - https://www.mendeley.com/catalogue/ed5eb2b3-ea5c-3305-a08b-8fa4b803568c/
U2 - 10.1016/j.jhydrol.2024.130948
DO - 10.1016/j.jhydrol.2024.130948
M3 - Article
AN - SCOPUS:85186328421
SN - 0022-1694
VL - 633
JO - Journal of Hydrology
JF - Journal of Hydrology
M1 - 130948
ER -