Kewei Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Kewei Chen has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- G. E. Hammond
Sandia National Laboratory, Albuquerque, NM 87123, USA
1 shared publication - Mohammad A. Moghaddam
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
1 shared publication - Ty Paul Andrew Ferre
Department of Hydrology and Atmospheric Science, University of Arizona, 1133 E. James E. Rogers Way, JW Harshbarger Building, Tucson, AZ 85721, USA
1 shared publication - Xingyuan Chen
Pacific Northwest National Laboratory Richland WA USA
1 shared publication - Xuehang Song
Pacific Northwest National Laboratory, Richland, WA 99352, USA
1 shared publication
Latest Publications
- Open Access
Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?
Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.
Open AccessCan Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?
Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.