no code implementations • 29 May 2021 • Mohsen Shahhosseini, Guiping Hu, Saeed Khaki, Sotirios V. Archontoulis
Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles.
no code implementations • 1 Sep 2020 • Mohsen Shahhosseini, Guiping Hu
Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models.
no code implementations • 28 Jul 2020 • Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis, Isaiah Huber
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt.
no code implementations • 18 Jan 2020 • Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis
The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields.
1 code implementation • 14 Aug 2019 • Mohsen Shahhosseini, Guiping Hu, Hieu Pham
To this end, an optimization-based nested algorithm that considers tuning hyperparameters as well as finding the optimal weights to combine ensembles (Generalized Weighted Ensemble with Internally Tuned Hyperparameters (GEM-ITH)) is designed.
no code implementations • 14 Aug 2019 • Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios V. Archontoulis
We asked: 1) How well do ML meta-models predict maize yield and N losses using pre-season information?