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Lucy Marshall

Macquarie University

North Ryde

Sydney, 2109

Australia

SCHOLARLY PAPERS

8

DOWNLOADS

425

TOTAL CITATIONS

1

Scholarly Papers (8)

1.

Subgrid Informed Neural Networks for High-Resolution Flood Mapping

Number of pages: 62 Posted: 07 Nov 2024 Last Revised: 25 Jun 2025
The University of Sydney - School of Civil Engineering, Macquarie University, Delft University of Technology, National University of Singapore (NUS) - School of Computing and University of Melbourne
Downloads 109 (649,907)

Abstract:

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Flood mapping, U-Net, Physics-informed machine learning, Hybrid models, Subgrid, Super-resolution

2.

Exploring graph neural networks for flood modeling: Challenges, opportunities, and future prospects

Number of pages: 30 Posted: 07 Jan 2026
affiliation not provided to SSRN, The University of Sydney - School of Civil Engineering, Delft University of Technology, National University of Singapore (NUS) - School of Computing and Macquarie University
Downloads 108 (664,543)
Citation 1

Abstract:

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Flood modeling, Surrogate Models, Machine Learning, Deep Learning, Graph Neural Networks

3.

­­Can Lstm Neural Networks Learn Physically Meaningful Principles? A Case Study in Sandy Shoreline Modelling

Number of pages: 25 Posted: 10 Apr 2024
University of New South Wales (UNSW), University of New South Wales (UNSW), The University of Sydney and Macquarie University
Downloads 65 (916,100)

Abstract:

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Model Explainability, Model Transparency, machine learning, Shoreline Modelling, LSTM

4.

A Mixture of Experts Approach to Sandy Shoreline Modelling in Storm Dominated Systems

Number of pages: 33 Posted: 19 Mar 2025
University of New South Wales (UNSW), University of New South Wales (UNSW), The University of Sydney and Macquarie University
Downloads 44 (1,133,231)

Abstract:

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storms, Shoreline Modelling, Machine Learning, Mixture of Experts

5.

Co-Development of Deep Learning and Process-Based Eco-Hydrological Models for Enhanced Climate Resilience

Number of pages: 21 Posted: 04 Oct 2024
Hui Zou, Lucy Marshall and Ashish Sharma
University of New South Wales (UNSW), Macquarie University and University of New South Wales (UNSW) - School of Civil and Environmental Engineering
Downloads 38 (1,222,651)

Abstract:

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LAI modelling, LSTM, co-development, climate change

6.

Modelling Vegetation Dynamics for Future Climates in Australian Catchments: Comparison of a Conceptual Eco-Hydrological Modelling Approach with a Deep Learning Alternative

Number of pages: 25 Posted: 12 Mar 2024
University of New South Wales (UNSW), Macquarie University, University of New South Wales (UNSW) - School of Civil and Environmental Engineering, University of New South Wales (UNSW) and Western Sydney University
Downloads 29 (1,332,057)

Abstract:

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Leaf Area Index, Modelling, deep learning, Climate change, Australia

7.

UrbanFloodBench: Bridging AI and Hydrology through Benchmarking of Coupled 1D–2D Urban Flood Surrogate Models

Number of pages: 40 Posted: 08 Jun 2026
National University of Singapore, The University of Sydney - School of Civil Engineering, National University of Singapore (NUS) - School of Computing, Macquarie University, University of New South Wales (UNSW), Delft University of Technology, Independent, National University of Singapore, affiliation not provided to SSRN, affiliation not provided to SSRN, Independent, Independent, Woxsen University, affiliation not provided to SSRN, affiliation not provided to SSRN and Oles Honchar Dnipro National University
Downloads 17

Abstract:

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Urban Flood Modelling, 1D-2D Benchmark Dataset, Surrogate models, Machine Learning, Deep Learning, Kaggle Competition

8.

Hydrologically constrained genetic programming for interpretable rainfall--runoff model discovery: A process-informed machine learning approach

Number of pages: 49 Posted: 09 Apr 2026
affiliation not provided to SSRN, The University of Sydney - School of Civil Engineering, Delft University of Technology, Macquarie University and National University of Singapore (NUS)
Downloads 15 (1,500,778)

Abstract:

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genetic programming, rainfall-runoff modelling, process-informed machine learning, conceptual hydrological models, model structure discovery