Chaopeng Shen

Assistant Professor

Pennsylvania State University

United States

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Dr. Chaopeng Shen is an Assistant Professor at Pennsylvania State University. His research focuses on hydrological modeling, data assimilation methods, and the application of machine learning in environmental contexts. Recent articles by Dr. Shen explore the impacts of data fusion on hydrologic modeling, enhance flood forecasting techniques, and examine the effects of topography on landslide susceptibility predictions.

Chaopeng Shen

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Articles (20)

Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models

Improving differentiable hydrologic modeling with interpretable forcing fusion

Deep Learning and Remote‐Sensed Observations Reveal Global Underestimation of River Obstructions

The Value of Terrain Pattern, High‐Resolution Data and Ensemble Modeling for Landslide Susceptibility Prediction

Probabilistic Diffusion Models Advance Extreme Flood Forecasting

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?

Global Daily Discharge Estimation Based on Grid Long Short‐Term Memory (LSTM) Model and River Routing

Calibration of the High‐Resolution Common Land Model in Simulating the Soil Moisture Over the Northeastern China Using an Adaptive Parameter Learning Method

Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?

Applying Knowledge-Guided Machine Learning to Slope Stability Prediction

Differentiable modelling to unify machine learning and physical models for geosciences

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Hybrid forecasting: blending climate predictions with AI models

Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats

Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation

Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers

Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy

Collaborators (12)

Daniel Kifer

Alexandre M. Tartakovsky

Marie-Amélie Boucher

Andrew Wood

Linda Speight

Yalan Song

Peishi Jiang

Robert Wilby

Li Li

Louise Slater

Te Pei

Paul Ullrich

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