A novel runoff prediction model based on support vector machine and gate recurrent unit with secondary mode decomposition
J Dong, Z Wang, J Wu, X Cui, R Pei - Water Resources Management, 2024 - Springer
Predicting runoff, one of the fundamental operations in hydrology, is crucial for directing the
complete exploitation and use of local water resources. However, influenced by factors such …
complete exploitation and use of local water resources. However, influenced by factors such …
Multi-angle property analysis and stress–strain curve prediction of cementitious sand gravel based on triaxial test
Q Tian, L Guo, Y Zhang, H Gao, Z Li - Scientific Reports, 2024 - nature.com
In order to further promote the application of cementitious sand gravel (CSG), the
mechanical properties and variation rules of CSG material under triaxial test were studied …
mechanical properties and variation rules of CSG material under triaxial test were studied …
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
Geoscientific models are simplified representations of complex earth and environmental
systems (EESs). Compared with physics-based numerical models, data-driven modeling …
systems (EESs). Compared with physics-based numerical models, data-driven modeling …
Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling
As surface water resources in the US continue to be pressured by excess nutrients carried
by agricultural runoff, the need to assess runoff risk at the field scale continues to grow in …
by agricultural runoff, the need to assess runoff risk at the field scale continues to grow in …
Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios
The present study investigates the ability of five boosting algorithms, namely Adaptive
Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost) …
Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost) …
A methodological framework for improving the performance of data-driven models, a case study for daily runoff prediction in the Maumee domain, US
Geoscientific models are simplified representations of complex earth and environmental
systems (EESs). Compared with physics-based numerical models, data-driven modeling …
systems (EESs). Compared with physics-based numerical models, data-driven modeling …