Huajin Chen

TL;DR: In this article, a novel machine learning algorithm, Geographically-Weighted Gradient Boosting Machine (GW-GBM), was developed by improving GBM through building spatial smoothing kernels to weigh the loss function.

TL;DR: This study not only provides valuable information for the development of biological weed control plan in the Delta, but also serves as a foundation for the continued research on calibration, evaluation, and uncertainty analysis of spatially distributed, physically based hydrologic models.

TL;DR: The study not only provides more detailed and accurate characterization of the storm-period response of E. coli across an urban and rural gradient, but also lays a foundation for predicting the concentration of E coli in practice, potentially suggesting effective watershed management decisions.

TL;DR: In this article, a meta-regression model was developed to predict VFS pesticide retention efficiency based on hydrologic responses of VFS's, incoming pollutant characteristics and the interaction within and between these two factor groups (R(2)=0.83).

TL;DR: In this paper, two distance metrics defined as the time taken by water flow to travel between two locations are developed, and replace the spatial distance metric or Euclidean distance that is currently used to interpolate topography.