HelioWebChaopeng Shen
flowchart
A[Chaopeng Shen]
AC["Associated Concepts (23)"]
AW["Authored Works (28)"]
CA["Linked Co-Authors (31)"]
CI["Linked Collaborating Institutions (30)"]
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AW== author ==>CA
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click AW "#authored-works"
click CA "#co-authors"
click CI "#collaborating-institutions"
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- ORCiD
- https://orcid.org/0000-0002-0685-1901
- OpenAlex ID
- https://openalex.org/A5067105881 (API record)
Associated Concepts [?]
- Computer science
- Geology
- Geography
- Physics
- Biology
- Mathematics
- Environmental science
- Engineering
- Ecology
- Cartography
- Machine learning
- Geotechnical engineering
- Artificial intelligence
- Statistics
- Quantum mechanics
- Hydrology (agriculture)
- Mathematical analysis
- Climatology
- Drainage basin
- Meteorology
- Operating system
- Streamflow
- Philosophy
Authored Works
sorted by decreasing year, and then by display-name
- The hitchhiker's guide to differentiable modeling in hydrology.
- The Impacts of Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Prediction
- Temperature Outweighs Light and Flow as the Predominant Control of Dissolved Oxygen across US Rivers
- Patterns and Drivers of Riverine Nitrate Concentrations in the Contiguous United States
- Opportunities for using Artificial Intelligence and Machine Learning to Address Hydrological Grand Challenges
- Large Scale Prediction of Channel Roughness Coefficient Using Machine Learning
- LSTM-based Integration of Recent Observations to Dramatically Improve One-month-ahead Point-based Snow Water Equivalent Forecast and Separate Error Sources
- Improving Large-Basin Streamflow Simulation Using a Differentiable, Learnable Routing Model
- From Parameter Learning to Structure Evolution: A Deep Integration of Physical Models and Deep Learning to Improve Hydrologic Modeling at Large Scales
- Differentiable modeling in Geosciences - Breaking down the imaginary barrier between machine learning and process-based modeling with differentiable modeling
- Deep Learning-based Models for Estimating River Channel Width
- Data Driven Approaches for Estimating River Channel Geometry over the Continental United States
- Can Sensor-based High-Frequency Nitrate Data Contribute to Estimating Continuous Daily Nitrate Concentration at Locations with Limited Nitrate Data Using Deep Learning?
- A high-quality deep-learning-based historical and near-real-time snow water equivalent product fusing satellite snow cover and in-situ data
- A global multitask deep learning soil moisture model for disaster management
- A Differentiable Ecosystem Modeling Framework for Highly Efficient Forward and Inverse Problems
- A Continental-scale Deep Learning Model for Total Phosphorus Reveals Progress Toward Water Quality Goals over the Past 40 Years in the United States
- The predominant control of land surface interactions on dissolved oxygen in rivers at the conterminous United State
- The Value of Deep Learning to the Future SWOT Mission for Improving Predictions in Ungauged Basins
- Role of Hydroelectric Power on Electricity Price and Storage Capacity for the Independent System Operator in New England
- Process learning of stream temperature modelling using deep learning and big data
- Predicting corn phenology shifts to reduce crop production risk under future climate scenarios
- Learning hydrologic dynamics from big data with sensitivity analysis and differentiable hydrology
- Improving Daily Streamflow Forecasting Systems in Data-scarce Regions with A Long Short-term Memory Model
- How to beat your teachers in hydrologic machine learning
- Discovering Localized River Parameters via Physics-Guided Machine Learning and the Muskingum-Cunge Method
- Climate Modulates the Response of Stream Nitrate Concentrations to Anthropogenic Nitrogen Addition in Conterminous United States (CONUS)
- A new rainfall-induced deep learning strategy for landslide susceptibility prediction
Linked Co-Authors
- Adnan Rajib
- Alain N. Rousseau
- Alexander Y. Sun
- Alexandre M. Tartakovsky
- Alison P. Appling
- Charuleka Varadharajan
- Dan Lu
- E. A. Meselhe
- E. W. Boyer
- Fabrizio Fenicia
- Farshid Rahmani
- H. V. Gupta
- Helen Weierbach
- J. David Moulton
- J. M. Duncan
- Jared Willard
- Jiangtao Liu
- Jitendra Kumar
- K. J. Van Meter
- Kathryn Lawson
- Kumar Sadayappan
- Lauren E. Koenig
- Marvin Höge
- Md Abdullah Al Mehedi
- Mohammed Ombadi
- S. L. Painter
- Samantha Oliver
- Soumendra N. Bhanja
- Tadd Bindas
- Wei Ren
- Xingyuan Chen
Linked Collaborating Institutions
- Boise State University, Idaho
- Dalian University of Technology, China
- Eberhard Karls University of Tubingen, Germany
- Environment Canada
- Lawrence Berkeley National Laboratory, California
- Los Alamos National Laboratory, New Mexico
- Luxembourg Institute of Science and Technology
- National Cheng Kung University, Taiwan
- National Institute for Scientific Research, Canada
- Oak Ridge National Laboratory, Tennessee
- Pacific Northwest National Laboratory
- Pennsylvania State University
- Sichuan University, China
- Swiss Federal Institute of Aquatic Science and Technology
- Texas A&M University, Kingsville
- Tufts University, Massachusetts
- Tulane University, Louisiana
- U.S. Geological Survey
- UMass Amherst
- University of Alabama, Tuscaloosa
- University of Arizona
- University of Connecticut
- University of Illinois, Urbana-Champaign
- University of Kentucky
- University of Miami, Florida
- University of Minnesota, Twin Cities
- University of South Carolina
- University of Texas, Austin
- University of Tokyo, Japan
- Villanova University, Pennsylvania
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