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Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost.
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The trmm multisatellite precipitation analysis (tmpa): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales
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Robust change vector analysis (rcva) for multi-sensor very high resolution optical satellite data
Thonfeld, F., Feilhauer, H., Braun, M., Menz, G., 2016 · 2016
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A hierarchical split-based approach for parametric thresholding of sar images: Flood inundation as a test case
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Flood inundation modelling: A review of methods, recent advances and uncertainty analysis
Teng, J., Jakeman, A.J., Vaze, J., Croke, B.F., Dutta, D., Kim, S., 2017 · 2017
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A high-accuracy map of global terrain elevations
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J.C., Sampson, C.C., Kanae, S., Bates, P.D., 2017 · 2017
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Estimating floodwater depths from flood inundation maps and topography
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Sentinel-1 grd preprocessing workflow, in: International Electronic Conference on Remote Sensing, MDPI. p. 11
Filipponi, F., 2019 · 2019
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Simultaneous calibration of multiple hydrodynamic model parameters using satellite altimetry observations of water surface elevation in the songhua river
Jiang, L., Madsen, H., Bauer-Gottwein, P., 2019 · 2019
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Spline-pinn: Approaching pdes without data using fast, physics-informed hermite-spline cnns
Wandel, N., Weinmann, M., Neidlin, M., Klein, R., 2021 · 2021
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Toward improved comparisons between land-surface-water-area estimates from a global river model and satellite observations
Zhou, X., Prigent, C., Yamazaki, D., 2021 · 2021
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Deep learning methods for flood mapping: a review of existing applications and future research directions
Bentivoglio, R., Isufi, E., Jonkman, S.N., Taormina, R., 2022 · 2022
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The west is ignoring Pakistan’s super-floods. Heed this warning: tomorrow it will be you
Bhutto, 2022 · 2022
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Physics-informed neural networks for the shallow-water equations on the sphere
Bihlo, A., Popovych, R.O., 2022 · 2022
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Deep fluids: A generative network for parameterized fluid simulations, in: Computer Graphics Forum, Wiley Online Library. pp. 59–70
Kim, B., Azevedo, V.C., Thuerey, N., Kim, T., Gross, M., Solenthaler, B., 2019 · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Raissi, M., Perdikaris, P., Karniadakis, G.E., 2019 · 2019
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Unsupervised deep change vector analysis for multiple-change detection in vhr images
Saha, S., Bovolo, F., Bruzzone, L., 2019 · 2019
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Neural operator: Graph kernel network for partial differential equations, in: ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations
Anandkumar, A., Azizzadenesheli, K., Bhattacharya, K., Kovachki, N., Li, Z., Liu, B., Stuart, A., 2020 · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al., 2020 · 2020
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Limitations of physics informed machine learning for nonlinear two-phase transport in porous media
Fuks, O., Tchelepi, H.A., 2020 · 2020
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Fourier neural operator for parametric partial differential equations, in: International Conference on Learning Representations
Li, Z., Kovachki, N.B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., Anandkumar, A., et al., 2020 · 2020
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Physics-informed neural networks (pinns) for fluid mechanics: A review
Cai, S., Mao, Z., Wang, Z., Yin, M., Karniadakis, G.E., 2022 · 2022
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Physics-informed graph neural galerkin networks: A unified framework for solving pde-governed forward and inverse problems
Gao, H., Zahr, M.J., Wang, J.X., 2022 · 2022
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Pakistan flood of 2022: Assessment using suite of satellite sensors and hydrological modelling
Gupta, P.K., Dubey, A.K., Pradhan, R., Chander, S., Singh, N., Jha, V.B., Gujrati, A., Wadhwa, C., Desai, N.M., 2022 · 2022
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A 30 m global map of elevation with forests and buildings removed
Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022 · 2022
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Multiscale framework for rapid change analysis from sar image time series: Case study of flood monitoring in the central coast regions of vietnam
Lê, T.T., Froger, J.L., Minh, D.H.T., 2022 · 2022
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Fourier neural operator with learned deformations for pdes on general geometries
Li, Z., Huang, D.Z., Liu, B., Anandkumar, A., 2022 · 2022
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A flood-crest forecast prototype for river floods using only in-stream measurements
Muste, M., Kim, D., Kim, K., 2022 · 2022
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The pakistan flood of august 2022: Causes and implications
Nanditha, J., Kushwaha, A.P., Singh, R., Malik, I., Solanki, H., Chuphal, D.S., Dangar, S., Mahto, S.S., Vegad, U., Mishra, V., 2023 · 2022
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Review of gpm imerg performance: A global perspective
Pradhan, R.K., Markonis, Y., Godoy, M.R.V., Villalba-Pradas, A., Andreadis, K.M., Nikolopoulos, E.I., Papalexiou, S.M., Rahim, A., Tapiador, F.J., Hanel, M., 2022 · 2022
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Sentinel-1 based analysis of the pakistan flood in 2022
Roth, F., Bauer-Marschallinger, B., Tupas, M.E., Reimer, C., Salamon, P., Wagner, W., 2022 · 2022
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A framework for mechanistic flood inundation forecasting at the metropolitan scale
Schubert, J.E., Luke, A., AghaKouchak, A., Sanders, B.F., 2022 · 2022
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Metaformer is actually what you need for vision, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10819–10829
Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., Yan, S., 2022 · 2022
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Physics-informed neural networks of the saint-venant equations for downscaling a large-scale river model
Feng, D., Tan, Z., He, Q., 2023 · 2023
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Supercharging hydrodynamic inundation models for instant flood insight
Fraehr, N., Wang, Q.J., Wu, W., Nathan, R., 2023 · 2023
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Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms
He, J., Zhang, L., Xiao, T., Wang, H., Luo, H., 2023 · 2023
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Domain agnostic fourier neural operators
Liu, N., Jafarzadeh, S., Yu, Y., 2023 · 2023
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Applications of physics informed neural operators
Rosofsky, S.G., Al Majed, H., Huerta, E., 2023 · 2023
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Disasternets: Embedding machine learning in disaster mapping, in: IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, IEEE. pp. 2536–2539
Xu, Q., Shi, Y., Zhu, X.X., 2023d · 2023
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Skilful nowcasting of extreme precipitation with nowcastnet
Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M.I., Wang, J., 2023 · 2023
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