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Abhishek Saha

Delft University of Technology

Stevinweg 1

Stevinweg 1

Delft, 2628 CN

Netherlands

SCHOLARLY PAPERS

5

DOWNLOADS

302

TOTAL CITATIONS

1

Scholarly Papers (5)

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.

Horton Based Infiltration Models Compared for Integration with a Sub-Grid Shallow Water Equation Solver for High-Resolution Flooding Simulation

Number of pages: 44 Posted: 28 Dec 2024
Abhishek Saha, Guus Stelling and Cornelis Vuik
Delft University of Technology, affiliation not provided to SSRN and Delft University of Technology
Downloads 56 (1,000,027)

Abstract:

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Horton infiltration model, SWMM, shallow water equations, sub-grid, rainfall-runoff, numerical integration

4.

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

5.

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 14

Abstract:

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