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Picture of Hoshin Gupta

Hoshin Gupta

Professor, Hydrology / Atmospheric Sciences | Regents Professor, Hydrology / Atmospheric Sciences | Professor, Global Change - GIDP | Professor, Remote Sensing / Spatial Analysis - GIDP | Srp Professor, Technology-Public Policy/Markets | Member of the Graduate Faculty

Hydrology & Atmospheric Sci

Collaboration(44)

Hao Zhang
Nirav Merchant
Sanlyn Buxner
Adam Douglas Henry
Xubin Zeng

Page 1 of 9

Grants(8)

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Publications(330)

Recent

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

2024

machine learning, geosciences, modeling, perceptron, mass conservation

Development of Groundwater Levels Dataset for Chile since 1970

2024

groundwater, water management, data collection, chile, geoscience

Position Paper: Bridging the GaBetween Machine Learning and Sensitivity Analysis

2024

machine learning, sensitivity analysis, bridging methods, integration techniques, data analysis

Rainfall distributional properties control hydrologic model parameter importance.

2024

rainfall, hydrologic modeling, parameter importance, distributional properties, hydrology

Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron

2024

hydrological modeling, catchment scale, interpretable modelling, mass-conserving, physical-conceptual

Virtual Hydrological Laboratories: Developing the Next Generation of Conceptual Models to Support Decision Making Under Change

2024

hydrological modeling, decision making, conceptual models

A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems

2023

machine learning, geoscientific modeling, perceptron, mass conservation, geoscientific systems

Calling for a National Model Benchmarking Facility

2023

benchmarking, national facility, research collaboration, technology innovation, government policy

Differentiable modelling to unify machine learning and physical models for geosciences

2023

machine learning, geosciences, physical models

em A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models

2023

hydrological modeling, variability, model development