Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4345-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4345-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
25 Aug 2022
Review article |
| 25 Aug 2022
| 25 Aug 2022
Deep learning methods for flood mapping: a review of existing applications and future research directions
Roberto Bentivoglio
CORRESPONDING AUTHOR
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Elvin Isufi
Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
Sebastian Nicolaas Jonkman
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Riccardo Taormina
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Related authors
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 27, 4227–4246, https://doi.org/10.5194/hess-27-4227-2023, https://doi.org/10.5194/hess-27-4227-2023, 2023
Short summary
Short summary
To overcome the computational cost of numerical models, we propose a deep-learning approach inspired by hydraulic models that can simulate the spatio-temporal evolution of floods. We show that the model can rapidly predict dike breach floods over different topographies and breach locations, with limited use of ground-truth data.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-614, https://doi.org/10.5194/hess-2021-614, 2021
Manuscript not accepted for further review
Short summary
Short summary
Deep Learning methods have been increasingly used in flood mapping as an alternative to traditional modeling techniques. While promising results have been obtained, our review shows significant challenges in building Deep Learning models that can generalize across multiple scenarios, account for complex interactions, and provide probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in Deep Learning.
Nienke Tessa Tempel, Laurene Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
EGUsphere, https://doi.org/10.5194/egusphere-2024-115, https://doi.org/10.5194/egusphere-2024-115, 2024
Short summary
Short summary
This study explores the impact of climatic variability on root zone water storage capacities thus on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 27, 4227–4246, https://doi.org/10.5194/hess-27-4227-2023, https://doi.org/10.5194/hess-27-4227-2023, 2023
Short summary
Short summary
To overcome the computational cost of numerical models, we propose a deep-learning approach inspired by hydraulic models that can simulate the spatio-temporal evolution of floods. We show that the model can rapidly predict dike breach floods over different topographies and breach locations, with limited use of ground-truth data.
Hanqing Xu, Elisa Ragno, Sebastiaan N. Jonkman, Jun Wang, Jeremy D. Bricker, Zhan Tian, and Laixiang Sun
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-261, https://doi.org/10.5194/hess-2023-261, 2023
Preprint under review for HESS
Short summary
Short summary
A coupled statistical-hydrodynamic model framework is used to quantitatively assess the sensitivity of the compound flood hazard to the relative timing between peak surge and rainfall. The results indicate that the timing difference between storm surges and rainfall plays a crucial role in flood inundation depth and extension. The most severe inundation occurs when rainfall precedes the storm surge peak of 2 hours.
Julius Schlumberger, Christian Ferrarin, Sebastiaan N. Jonkman, Manuel Andres Diaz Loaiza, Alessandro Antonini, and Sandra Fatorić
Nat. Hazards Earth Syst. Sci., 22, 2381–2400, https://doi.org/10.5194/nhess-22-2381-2022, https://doi.org/10.5194/nhess-22-2381-2022, 2022
Short summary
Short summary
Flooding has serious impacts on the old town of Venice. This paper presents a framework combining a flood model with a flood-impact model to support improving protection against future floods in Venice despite the recently built MOSE barrier. Applying the framework to seven plausible flood scenarios, it was found that individual protection has a significant damage-mediating effect if the MOSE barrier does not operate as anticipated. Contingency planning thus remains important in Venice.
Manuel Andres Diaz Loaiza, Jeremy D. Bricker, Remi Meynadier, Trang Minh Duong, Rosh Ranasinghe, and Sebastiaan N. Jonkman
Nat. Hazards Earth Syst. Sci., 22, 345–360, https://doi.org/10.5194/nhess-22-345-2022, https://doi.org/10.5194/nhess-22-345-2022, 2022
Short summary
Short summary
Extratropical cyclones are one of the major causes of coastal floods in Europe and the world. Understanding the development process and the flooding of storm Xynthia, together with the damages that occurred during the storm, can help to forecast future losses due to other similar storms. In the present paper, an analysis of shallow water variables (flood depth, velocity, etc.) or coastal variables (significant wave height, energy flux, etc.) is done in order to develop damage curves.
Christopher H. Lashley, Sebastiaan N. Jonkman, Jentsje van der Meer, Jeremy D. Bricker, and Vincent Vuik
Nat. Hazards Earth Syst. Sci., 22, 1–22, https://doi.org/10.5194/nhess-22-1-2022, https://doi.org/10.5194/nhess-22-1-2022, 2022
Short summary
Short summary
Many coastlines around the world have shallow foreshores (e.g. salt marshes and mudflats) that reduce storm waves and the risk of coastal flooding. However, most of the studies that tried to quantify this effect have excluded the influence of very long waves, which often dominate in shallow water. Our newly developed framework addresses this oversight and suggests that safety along these coastlines may be overestimated, since these very long waves are largely neglected in flood risk assessments.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-614, https://doi.org/10.5194/hess-2021-614, 2021
Manuscript not accepted for further review
Short summary
Short summary
Deep Learning methods have been increasingly used in flood mapping as an alternative to traditional modeling techniques. While promising results have been obtained, our review shows significant challenges in building Deep Learning models that can generalize across multiple scenarios, account for complex interactions, and provide probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in Deep Learning.
Erik C. van Berchum, Mathijs van Ledden, Jos S. Timmermans, Jan H. Kwakkel, and Sebastiaan N. Jonkman
Nat. Hazards Earth Syst. Sci., 20, 2633–2646, https://doi.org/10.5194/nhess-20-2633-2020, https://doi.org/10.5194/nhess-20-2633-2020, 2020
Short summary
Short summary
Flood risk management is especially complicated in coastal cities. The complexity of multiple flood hazards in a rapidly changing urban environment leads to a situation with many different potential measures and future scenarios. This research demonstrates a new model capable of quickly simulating flood impact and comparing many different strategies. This is applied to the city of Beira, where it was able to provide new insights into the local flood risk and potential strategies.
Sebastiaan N. Jonkman, Maartje Godfroy, Antonia Sebastian, and Bas Kolen
Nat. Hazards Earth Syst. Sci., 18, 1073–1078, https://doi.org/10.5194/nhess-18-1073-2018, https://doi.org/10.5194/nhess-18-1073-2018, 2018
Short summary
Short summary
An analysis was made of the loss of life directly caused by Hurricane Harvey. Information was collected for 70 fatalities that occurred directly due to the event. Most of the fatalities occurred in the greater Houston area, which was most severely affected by extreme rainfall and heavy flooding. The majority of fatalities in this area were recovered outside the designated 100- and 500-year flood zones. Most fatalities occurred due to drowning (81 %), particularly in and around vehicles.
Dominik Paprotny, Michalis I. Vousdoukas, Oswaldo Morales-Nápoles, Sebastiaan N. Jonkman, and Luc Feyen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-132, https://doi.org/10.5194/hess-2018-132, 2018
Preprint withdrawn
Dominik Paprotny, Oswaldo Morales-Nápoles, and Sebastiaan N. Jonkman
Earth Syst. Sci. Data, 10, 565–581, https://doi.org/10.5194/essd-10-565-2018, https://doi.org/10.5194/essd-10-565-2018, 2018
Short summary
Short summary
Natural hazards affect areas with various population density, economic production value and preparedness. This database will help to assess the impact of hazards in Europe in a long-term perspective. It contains data on losses, dates and location of 1564 floods from 1870–2016 in 37 countries. For the same area and timeframe, land use, population and asset value were reconstructed. Combining both data sets, one can correct the amount of losses from past events for demographic and economic growth.
Dominik Paprotny, Oswaldo Morales-Nápoles, and Sebastiaan N. Jonkman
Nat. Hazards Earth Syst. Sci., 17, 1267–1283, https://doi.org/10.5194/nhess-17-1267-2017, https://doi.org/10.5194/nhess-17-1267-2017, 2017
A. Miller, S. N. Jonkman, and M. Van Ledden
Nat. Hazards Earth Syst. Sci., 15, 59–73, https://doi.org/10.5194/nhess-15-59-2015, https://doi.org/10.5194/nhess-15-59-2015, 2015
Short summary
Short summary
After Hurricane Katrina in 2005, New Orleans’ hurricane protection was improved to withstand a 1/100 per year hurricane. This paper quantifies the risk to life in post-Katrina New Orleans. When compared to risks of other large-scale engineering systems (e.g. other flood prone areas, dams and the nuclear sector) and acceptable risk criteria, results indicate the risk to life is significant. Results and methods of this study can inform future flood protection management and risk communication.
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Modelling approaches
A systematic review of climate change science relevant to Australian design flood estimation
Technical Note: Resolution enhancement of flood inundation grids
Floods and droughts: a multivariate perspective
Technical note: Statistical generation of climate-perturbed flow duration curves
Extreme floods in Europe: going beyond observations using reforecast ensemble pooling
Hydroinformatics education – the Water Informatics in Science and Engineering (WISE) Centre for Doctoral Training
Wetropolis extreme rainfall and flood demonstrator: from mathematical design to outreach
Technical note: The beneficial role of a natural permeable layer in slope stabilization by drainage trenches
Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects
Observation operators for assimilation of satellite observations in fluvial inundation forecasting
Contribution of potential evaporation forecasts to 10-day streamflow forecast skill for the Rhine River
Inundation mapping based on reach-scale effective geometry
Effects of variability in probable maximum precipitation patterns on flood losses
The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data
A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction
Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset
Technical note: Design flood under hydrological uncertainty
Topography- and nightlight-based national flood risk assessment in Canada
Regime shifts in annual maximum rainfall across Australia – implications for intensity–frequency–duration (IFD) relationships
Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs
A continuous rainfall model based on vine copulas
Estimates of global dew collection potential on artificial surfaces
Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes
An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics
Modeling root reinforcement using a root-failure Weibull survival function
Socio-hydrology: conceptualising human-flood interactions
Application of a model-based rainfall-runoff database as efficient tool for flood risk management
Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis
HydroViz: design and evaluation of a Web-based tool for improving hydrology education
Web 2.0 collaboration tool to support student research in hydrology – an opinion
SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds – the two-CN system approach
Discharge estimation combining flow routing and occasional measurements of velocity
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application
Comment on "A praxis-oriented perspective of streamflow inference from stage observations – the method of Dottori et al. (2009) and the alternative of the Jones Formula, with the kinematic wave celerity computed on the looped rating curve" by Koussis (2009)
An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting
Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 28, 1251–1285, https://doi.org/10.5194/hess-28-1251-2024, https://doi.org/10.5194/hess-28-1251-2024, 2024
Short summary
Short summary
In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Seth Bryant, Guy Schumann, Heiko Apel, Heidi Kreibich, and Bruno Merz
Hydrol. Earth Syst. Sci., 28, 575–588, https://doi.org/10.5194/hess-28-575-2024, https://doi.org/10.5194/hess-28-575-2024, 2024
Short summary
Short summary
A new algorithm has been developed to quickly produce high-resolution flood maps. It is faster and more accurate than current methods and is available as open-source scripts. This can help communities better prepare for and mitigate flood damages without expensive modelling.
Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 27, 2479–2497, https://doi.org/10.5194/hess-27-2479-2023, https://doi.org/10.5194/hess-27-2479-2023, 2023
Short summary
Short summary
I discuss different types of multivariate hydrological extremes and their dependencies, including regional extremes affecting multiple locations, such as spatially connected flood events; consecutive extremes occurring in close temporal succession, such as successive droughts; extremes characterized by multiple characteristics, such as floods with jointly high peak discharge and flood volume; and transitions between different types of extremes, such as drought-to-flood transitions.
Veysel Yildiz, Robert Milton, Solomon Brown, and Charles Rougé
Hydrol. Earth Syst. Sci., 27, 2499–2507, https://doi.org/10.5194/hess-27-2499-2023, https://doi.org/10.5194/hess-27-2499-2023, 2023
Short summary
Short summary
The proposed approach is based on the parameterisation of flow duration curves (FDCs) to generate hypothetical streamflow futures. (1) We sample a broad range of future climates with modified values of three key streamflow statistics. (2) We generate an FDC for each hydro-climate future. (3) The resulting ensemble is ready to support robustness assessments in a changing climate. Our approach seamlessly represents a large range of futures with increased frequencies of both high and low flows.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
Short summary
Short summary
Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Thorsten Wagener, Dragan Savic, David Butler, Reza Ahmadian, Tom Arnot, Jonathan Dawes, Slobodan Djordjevic, Roger Falconer, Raziyeh Farmani, Debbie Ford, Jan Hofman, Zoran Kapelan, Shunqi Pan, and Ross Woods
Hydrol. Earth Syst. Sci., 25, 2721–2738, https://doi.org/10.5194/hess-25-2721-2021, https://doi.org/10.5194/hess-25-2721-2021, 2021
Short summary
Short summary
How can we effectively train PhD candidates both (i) across different knowledge domains in water science and engineering and (ii) in computer science? To address this issue, the Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering.
Onno Bokhove, Tiffany Hicks, Wout Zweers, and Thomas Kent
Hydrol. Earth Syst. Sci., 24, 2483–2503, https://doi.org/10.5194/hess-24-2483-2020, https://doi.org/10.5194/hess-24-2483-2020, 2020
Short summary
Short summary
Wetropolis is a
table-topdemonstration model with extreme rainfall and flooding, including random rainfall, river flow, flood plains, an upland reservoir, a porous moor, and a city which can flood. It lets the viewer experience extreme rainfall and flood events in a physical model on reduced spatial and temporal scales with an event return period of 6.06 min rather than, say, 200 years. We disseminate its mathematical design and how it has been shown most prominently to over 500 flood victims.
Gianfranco Urciuoli, Luca Comegna, Marianna Pirone, and Luciano Picarelli
Hydrol. Earth Syst. Sci., 24, 1669–1676, https://doi.org/10.5194/hess-24-1669-2020, https://doi.org/10.5194/hess-24-1669-2020, 2020
Short summary
Short summary
The aim of this paper is to demonstrate, through a numerical approach, that the presence of soil layers of higher permeability, a not unlikely condition in some deep landslides in clay, may be exploited to improve the efficiency of systems of drainage trenches for slope stabilization. The problem has been examined for the case that a unique pervious layer, parallel to the ground surface, is present at an elevation higher than the bottom of the trenches.
Bin Xiong, Lihua Xiong, Jun Xia, Chong-Yu Xu, Cong Jiang, and Tao Du
Hydrol. Earth Syst. Sci., 23, 4453–4470, https://doi.org/10.5194/hess-23-4453-2019, https://doi.org/10.5194/hess-23-4453-2019, 2019
Short summary
Short summary
We develop a new indicator of reservoir effects, called the rainfall–reservoir composite index (RRCI). RRCI, coupled with the effects of static reservoir capacity and scheduling-related multivariate rainfall, has a better performance than the previous indicator in terms of explaining the variation in the downstream floods affected by reservoir operation. A covariate-based flood frequency analysis using RRCI can provide more reliable downstream flood risk estimation.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
Short summary
Short summary
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Bart van Osnabrugge, Remko Uijlenhoet, and Albrecht Weerts
Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019, https://doi.org/10.5194/hess-23-1453-2019, 2019
Short summary
Short summary
A correct estimate of the amount of future precipitation is the most important factor in making a good streamflow forecast, but evaporation is also an important component that determines the discharge of a river. However, in this study for the Rhine River we found that evaporation forecasts only give an almost negligible improvement compared to methods that use statistical information on climatology for a 10-day streamflow forecast. This is important to guide research on low flow forecasts.
Cédric Rebolho, Vazken Andréassian, and Nicolas Le Moine
Hydrol. Earth Syst. Sci., 22, 5967–5985, https://doi.org/10.5194/hess-22-5967-2018, https://doi.org/10.5194/hess-22-5967-2018, 2018
Short summary
Short summary
Inundation models are useful for hazard management and prevention. They are traditionally based on hydraulic partial differential equations (with satisfying results but large data and computational requirements). This study presents a simplified approach combining reach-scale geometric properties with steady uniform flow equations. The model shows promising results overall, although difficulties persist in the most complex urbanised reaches.
Andreas Paul Zischg, Guido Felder, Rolf Weingartner, Niall Quinn, Gemma Coxon, Jeffrey Neal, Jim Freer, and Paul Bates
Hydrol. Earth Syst. Sci., 22, 2759–2773, https://doi.org/10.5194/hess-22-2759-2018, https://doi.org/10.5194/hess-22-2759-2018, 2018
Short summary
Short summary
We developed a model experiment and distributed different rainfall patterns over a mountain river basin. For each rainfall scenario, we computed the flood losses with a model chain. The experiment shows that flood losses vary considerably within the river basin and depend on the timing of the flood peaks from the basin's sub-catchments. Basin-specific characteristics such as the location of the main settlements within the floodplains play an additional important role in determining flood losses.
Guillaume Le Bihan, Olivier Payrastre, Eric Gaume, David Moncoulon, and Frédéric Pons
Hydrol. Earth Syst. Sci., 21, 5911–5928, https://doi.org/10.5194/hess-21-5911-2017, https://doi.org/10.5194/hess-21-5911-2017, 2017
Short summary
Short summary
This paper illustrates how an integrated flash flood monitoring (or forecasting) system may be designed to directly provide information on possibly flooded areas and associated impacts on a very detailed river network and over large territories. The approach is extensively tested in the regions of Alès and Draguignan, located in south-eastern France. Validation results are presented in terms of accuracy of the estimated flood extents and related impacts (based on insurance claim data).
Ashley Wright, Jeffrey P. Walker, David E. Robertson, and Valentijn R. N. Pauwels
Hydrol. Earth Syst. Sci., 21, 3827–3838, https://doi.org/10.5194/hess-21-3827-2017, https://doi.org/10.5194/hess-21-3827-2017, 2017
Short summary
Short summary
The accurate reduction of hydrologic model input data is an impediment towards understanding input uncertainty and model structural errors. This paper compares the ability of two transforms to reduce rainfall input data. The resultant transforms are compressed to varying extents and reconstructed before being evaluated with standard simulation performance summary metrics and descriptive statistics. It is concluded the discrete wavelet transform is most capable of preserving rainfall time series.
Ricardo Zubieta, Augusto Getirana, Jhan Carlo Espinoza, Waldo Lavado-Casimiro, and Luis Aragon
Hydrol. Earth Syst. Sci., 21, 3543–3555, https://doi.org/10.5194/hess-21-3543-2017, https://doi.org/10.5194/hess-21-3543-2017, 2017
Short summary
Short summary
This paper indicates that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, in comparison to observed rainfall (by 11.1 % and 15.7 %, respectively). Statistical analysis indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets for estimating observed streamflows in Andean–Amazonian regions (Ucayali Basin, southern regions of the Amazon Basin of Peru and Ecuador).
Anna Botto, Daniele Ganora, Pierluigi Claps, and Francesco Laio
Hydrol. Earth Syst. Sci., 21, 3353–3358, https://doi.org/10.5194/hess-21-3353-2017, https://doi.org/10.5194/hess-21-3353-2017, 2017
Short summary
Short summary
The paper provides an easy-to-use implementation of the UNCODE framework, which allows one to estimate the design flood value by directly accounting for sample uncertainty. Other than a design tool, this methodology is also a practical way to quantify the value of data in the design process.
Amin Elshorbagy, Raja Bharath, Anchit Lakhanpal, Serena Ceola, Alberto Montanari, and Karl-Erich Lindenschmidt
Hydrol. Earth Syst. Sci., 21, 2219–2232, https://doi.org/10.5194/hess-21-2219-2017, https://doi.org/10.5194/hess-21-2219-2017, 2017
Short summary
Short summary
Flood mapping is one of Canada's major national interests. This work presents a simple and effective method for large-scale flood hazard and risk mapping, applied in this study to Canada. Readily available data, such as remote sensing night-light data, topography, and stream network were used to create the maps.
D. C. Verdon-Kidd and A. S. Kiem
Hydrol. Earth Syst. Sci., 19, 4735–4746, https://doi.org/10.5194/hess-19-4735-2015, https://doi.org/10.5194/hess-19-4735-2015, 2015
Short summary
Short summary
Rainfall intensity-frequency-duration (IFD) relationships are required for the design and planning of water supply and management systems around the world. Currently IFD information is based on the "stationary climate assumption". However, this paper provides evidence of regime shifts in annual maxima rainfall time series using 96 daily rainfall stations and 66 sub-daily rainfall stations across Australia. Importantly, current IFD relationships may under- or overestimate the design rainfall.
P. A. Marker, N. Foged, X. He, A. V. Christiansen, J. C. Refsgaard, E. Auken, and P. Bauer-Gottwein
Hydrol. Earth Syst. Sci., 19, 3875–3890, https://doi.org/10.5194/hess-19-3875-2015, https://doi.org/10.5194/hess-19-3875-2015, 2015
H. Vernieuwe, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 19, 2685–2699, https://doi.org/10.5194/hess-19-2685-2015, https://doi.org/10.5194/hess-19-2685-2015, 2015
H. Vuollekoski, M. Vogt, V. A. Sinclair, J. Duplissy, H. Järvinen, E.-M. Kyrö, R. Makkonen, T. Petäjä, N. L. Prisle, P. Räisänen, M. Sipilä, J. Ylhäisi, and M. Kulmala
Hydrol. Earth Syst. Sci., 19, 601–613, https://doi.org/10.5194/hess-19-601-2015, https://doi.org/10.5194/hess-19-601-2015, 2015
Short summary
Short summary
The global potential for collecting usable water from dew on an
artificial collector sheet was investigated by utilising 34 years of
meteorological reanalysis data as input to a dew formation model. Continental dew formation was found to be frequent and common, but daily yields were
mostly below 0.1mm.
D. E. Mora, L. Campozano, F. Cisneros, G. Wyseure, and P. Willems
Hydrol. Earth Syst. Sci., 18, 631–648, https://doi.org/10.5194/hess-18-631-2014, https://doi.org/10.5194/hess-18-631-2014, 2014
M. T. Pham, W. J. Vanhaute, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 5167–5183, https://doi.org/10.5194/hess-17-5167-2013, https://doi.org/10.5194/hess-17-5167-2013, 2013
M. Schwarz, F. Giadrossich, and D. Cohen
Hydrol. Earth Syst. Sci., 17, 4367–4377, https://doi.org/10.5194/hess-17-4367-2013, https://doi.org/10.5194/hess-17-4367-2013, 2013
G. Di Baldassarre, A. Viglione, G. Carr, L. Kuil, J. L. Salinas, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 3295–3303, https://doi.org/10.5194/hess-17-3295-2013, https://doi.org/10.5194/hess-17-3295-2013, 2013
L. Brocca, S. Liersch, F. Melone, T. Moramarco, and M. Volk
Hydrol. Earth Syst. Sci., 17, 3159–3169, https://doi.org/10.5194/hess-17-3159-2013, https://doi.org/10.5194/hess-17-3159-2013, 2013
T. A. McMahon, M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar
Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, https://doi.org/10.5194/hess-17-1331-2013, 2013
E. Habib, Y. Ma, D. Williams, H. O. Sharif, and F. Hossain
Hydrol. Earth Syst. Sci., 16, 3767–3781, https://doi.org/10.5194/hess-16-3767-2012, https://doi.org/10.5194/hess-16-3767-2012, 2012
A. Pathirana, B. Gersonius, and M. Radhakrishnan
Hydrol. Earth Syst. Sci., 16, 2499–2509, https://doi.org/10.5194/hess-16-2499-2012, https://doi.org/10.5194/hess-16-2499-2012, 2012
K. X. Soulis and J. D. Valiantzas
Hydrol. Earth Syst. Sci., 16, 1001–1015, https://doi.org/10.5194/hess-16-1001-2012, https://doi.org/10.5194/hess-16-1001-2012, 2012
G. Corato, T. Moramarco, and T. Tucciarelli
Hydrol. Earth Syst. Sci., 15, 2979–2994, https://doi.org/10.5194/hess-15-2979-2011, https://doi.org/10.5194/hess-15-2979-2011, 2011
A. Elshorbagy, G. Corzo, S. Srinivasulu, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 14, 1943–1961, https://doi.org/10.5194/hess-14-1943-2010, https://doi.org/10.5194/hess-14-1943-2010, 2010
A. D. Koussis
Hydrol. Earth Syst. Sci., 14, 1093–1097, https://doi.org/10.5194/hess-14-1093-2010, https://doi.org/10.5194/hess-14-1093-2010, 2010
J. A. Velázquez, T. Petit, A. Lavoie, M.-A. Boucher, R. Turcotte, V. Fortin, and F. Anctil
Hydrol. Earth Syst. Sci., 13, 2221–2231, https://doi.org/10.5194/hess-13-2221-2009, https://doi.org/10.5194/hess-13-2221-2009, 2009
Cited articles
Abdullah, M. F., Siraj, S., and Hodgett, R. E.: An Overview of Multi-Criteria
Decision Analysis (MCDA) Application in Managing Water-Related Disaster
Events: Analyzing 20 Years of Literature for Flood and Drought Events, Water,
13, 1358, https://doi.org/10.3390/w13101358, 2021. a
Ahmadlou, M., Al-Fugara, A., Al-Shabeeb, A., Arora, A., Al-Adamat, R., Pham,
Q., Al-Ansari, N., Linh, N., and Sajedi, H.: Flood susceptibility mapping and
assessment using a novel deep learning model combining multilayer perceptron
and autoencoder neural networks, J. Flood Risk Manage., 14, e12683,
https://doi.org/10.1111/jfr3.12683, 2021. a, b, c, d
Ahmed, N., Hoque, M. A.-A., Arabameri, A., Pal, S. C., Chakrabortty, R., and
Jui, J.: Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh
using deep boost, deep learning neural network, and artificial neural
network, Geocarto Int., 1–22, https://doi.org/10.1080/10106049.2021.2005698, 2021. a, b, c, d, e
Amini, J.: A method for generating floodplain maps using IKONOS images and
DEMs, Int. J. Remote Sens., 31, 2441–2456,
https://doi.org/10.1080/01431160902929230, 2010. a, b, c, d
Ávila, A., Justino, F., Wilson, A., Bromwich, D., and Amorim, M.: Recent
precipitation trends, flash floods and landslides in southern Brazil,
Environ. Res. Lett., 11, 114029, https://doi.org/10.1088/1748-9326/11/11/114029,
2016. a
Badrinarayanan, V., Kendall, A., and Cipolla, R.: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, 39, 2481–2495, https://doi.org/10.1109/TPAMI.2016.2644615, 2017. a, b
Balestriero, R., Pesenti, J., and LeCun, Y.: Learning in High Dimension Always
Amounts to Extrapolation, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.09485, 2021. a
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi,
V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R.,
Gulcehre, C., Song, F., Ballard, A., Gilmer, J., Dahl, G., Vaswani, A.,
Allen, K., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli,
P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R.: Relational
inductive biases, deep learning, and graph networks, arXiv [preprint], 1–40, https://doi.org/10.48550/arXiv.1806.01261, 2018. a, b
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D.: Weight
uncertainty in neural network, in: International Conference on Machine
Learning, PMLR, 1613–1622, 2015. a
Bobée, B. and Rasmussen, P. F.: Recent advances in flood frequency
analysis, Rev. Geophys., 33, 1111–1116, 1995. a
Bodnar, C., Frasca, F., Otter, N., Wang, Y., Lio, P., Montufar, G. F., and Bronstein, M.: Weisfeiler and Lehman go cellular: CW networks, Advances in Neural Information Processing Systems, 34, 2625–2640, 2021. a
Bomers, A., Schielen, R. M., and Hulscher, S. J.: The influence of grid shape
and grid size on hydraulic river modelling performance, Environ. Fluid
Mech., 19, 1273–1294, https://doi.org/10.1007/s10652-019-09670-4, 2019. a
Bonafilia, D., Tellman, B., Anderson, T., and Issenberg, E.: Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, 835–845, https://doi.org/10.1109/CVPRW50498.2020.00113, 2020. a
Bowes, B. D., Tavakoli, A., Wang, C., Heydarian, A., Behl, M., Beling, P. A.,
and Goodall, J. L.: Flood mitigation in coastal urban catchments using
real-time stormwater infrastructure control and reinforcement learning,
J. Hydroinfo., 23, 529–547, 2021. a
Bradley, A. P.: The use of the area under the ROC curve in the evaluation of
machine learning algorithms, Pattern Recog., 30, 1145–1159, 1997. a
Bronstein, M. M., Bruna, J., Lecun, Y., Szlam, A., and Vandergheynst, P.:
Geometric Deep Learning: Going beyond Euclidean data, IEEE Signal
Proc. Mag., 34, 18–42, https://doi.org/10.1109/MSP.2017.2693418, 2017. a
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P.: Geometric
deep learning: Grids, groups, graphs, geodesics, and gauges,
arXiv [preprint], https://doi.org/10.48550/arXiv.2104.13478, 2021. a, b
Candy, A. S.: A consistent approach to unstructured mesh generation for
geophysical models, arXiv [preprint], https://doi.org/10.48550/arXiv.1703.08491, 2017. a
Chakrabortty, R., Chandra Pal, S., Rezaie, F., Arabameri, A., Lee, S., Roy, P.,
Saha, A., Chowdhuri, I., and Moayedi, H.: Flash-flood hazard susceptibility
mapping in Kangsabati River Basin, India, Geocarto Int., 1–23, https://doi.org/10.1080/10106049.2021.1953618,
2021a. a, b, c
Chang, D.-L., Yang, S.-H., Hsieh, S.-L., Wang, H.-J., and Yeh, K.-C.:
Artificial intelligence methodologies applied to prompt pluvial flood
estimation and prediction, Water, 12, 3552, https://doi.org/10.3390/w12123552, 2020. a
Chen, J., Huang, G., and Chen, W.: Towards better flood risk management:
Assessing flood risk and investigating the potential mechanism based on
machine learning models, J. Environ. Manage., 112810, https://doi.org/10.1016/j.jenvman.2021.112810, 2021. a
Chicco, D. and Jurman, G.: The advantages of the Matthews correlation
coefficient (MCC) over F1 score and accuracy in binary classification
evaluation, BMC Genom., 21, 1–13, 2020. a
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, in: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, Association for Computational Linguistics, 103–111, 2014. a, b
Cian, F., Marconcini, M., and Ceccato, P.: Normalized Difference Flood Index
for rapid flood mapping: Taking advantage of EO big data, Remote Sens. Environ., 209, 712–730, https://doi.org/10.1016/j.rse.2018.03.006,
2018. a
Cortes, C., Mohri, M., and Syed, U.: Deep boosting, in: International
conference on machine learning, PMLR, 1179–1187, 2014. a
Costabile, P., Costanzo, C., and Macchione, F.: Performances and limitations
of the diffusive approximation of the 2-d shallow water equations for flood
simulation in urban and rural areas, Appl. Numer. Math., 116,
141–156, https://doi.org/10.1016/j.apnum.2016.07.003, 2017. a, b
Damianou, A. and Lawrence, N. D.: Deep gaussian processes, in: Artificial
intelligence and statistics, PMLR, 207–215, 2013. a
Darabi, H., Rahmati, O., Naghibi, S., Mohammadi, F., Ahmadisharaf, E.,
Kalantari, Z., Torabi Haghighi, A., Soleimanpour, S., Tiefenbacher, J., and
Tien Bui, D.: Development of a novel hybrid multi-boosting neural network
model for spatial prediction of urban flood, Geocarto Int., https://doi.org/10.1080/10106049.2021.1920629, 2021. a, b, c, d, e, f
de Brito, M. M. and Evers, M.: Multi-criteria decision-making for flood risk management: a survey of the current state of the art, Nat. Hazards Earth Syst. Sci., 16, 1019–1033, https://doi.org/10.5194/nhess-16-1019-2016, 2016. a
De Haan, P., Weiler, M., Cohen, T., and Welling, M.: Gauge equivariant mesh
CNNs anisotropic convolutions on geometric graphs, arXiv, https://doi.org/10.48550/arXiv.2003.05425, 2020. a
de Moel, H., van Alphen, J., and Aerts, J. C. J. H.: Flood maps in Europe – methods, availability and use, Nat. Hazards Earth Syst. Sci., 9, 289–301, https://doi.org/10.5194/nhess-9-289-2009, 2009. a
de Moel, H., Jongman, B., Kreibich, H., Merz, B., Penning-Rowsell, E., and
Ward, P. J.: Flood risk assessments at different spatial scales, Mitig. Adapt. Strat. Gl., 20, 865–890, 2015. a
Delgado, R. and Tibau, X.-A.: Why Cohen’s Kappa should be avoided as
performance measure in classification, PloS One, 14, e0222916, https://doi.org/10.1371/journal.pone.0222916, 2019. a
Destro, E., Amponsah, W., Nikolopoulos, E. I., Marchi, L., Marra, F.,
Zoccatelli, D., and Borga, M.: Coupled prediction of flash flood response
and debris flow occurrence: Application on an alpine extreme flood event,
J. Hydrol., 558, 225–237, https://doi.org/10.1016/j.jhydrol.2018.01.021,
2018. a
Di Baldassarre, G., Schumann, G., Bates, P. D., Freer, J. E., and Beven, K. J.:
Flood-plain mapping: a critical discussion of deterministic and probabilistic
approaches, J. Sci. Hydrol., 55, 364–376, 2010. a
Domeneghetti, A., Vorogushyn, S., Castellarin, A., Merz, B., and Brath, A.: Probabilistic flood hazard mapping: effects of uncertain boundary conditions, Hydrol. Earth Syst. Sci., 17, 3127–3140, https://doi.org/10.5194/hess-17-3127-2013, 2013. a, b
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., and Lim, W. M.: How to
conduct a bibliometric analysis: An overview and guidelines, J. Business Res., 133, 285–296, 2021. a
Dottori, F., Alfieri, L., Bianchi, A., Skoien, J., and Salamon, P.: A new dataset of river flood hazard maps for Europe and the Mediterranean Basin, Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, 2022. a
Ebli, S., Defferrard, M., and Spreemann, G.: Simplicial Neural Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.03633, 2020. a
European Union: Directive 2007/60/EC of the European Counil and European
Parliment of 23 October 2007 on the assessment and management of flood
risks, Official Journal of the European Union, 27–34,
http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32007L0060&from=EN (last access: 20 February 2022),
2007. a
Fang, Z., Yang, T., and Jin, Y.: DeepStreet: A deep learning powered urban
street network generation module, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.04365,
2020b. a
Fazeli-Varzaneh, M., Bettinger, P., Ghaderi-Azad, E., Kozak, M., Mafi-Gholami,
D., and Jaafari, A.: Forestry Research in the Middle East: A Bibliometric
Analysis, Sustainability, 13, 8261, https://doi.org/10.3390/su13158261, 2021. a
Ferraro, D., Costabile, P., Costanzo, C., Petaccia, G., and Macchione, F.: A
spectral analysis approach for the a priori generation of computational grids
in the 2-D hydrodynamic-based runoff simulations at a basin scale, J. Hydrol., 582, 124508, https://doi.org/10.1016/j.jhydrol.2019.124508, 2020. a
Ferreira, L. A., Fonseca, A. R., Lima, N. Z., Mesquita, R. C., and Salgado,
G. C.: Graphical interface for electromagnetic problem solving using meshless
methods, Journal of Microwaves, Optoelectron. Elec. Appl., 14, SI–54 to SI, 2015. a
Gama, F., Isufi, E., Leus, G., and Ribeiro, A.: Graphs, convolutions, and neural networks: From graph filters to graph neural networks, IEEE Signal Processing Magazine, 37, 128–138, 2020. a
Glenis, V., McGough, A. S., Kutija, V., Kilsby, C., and Woodman, S.: Flood
modelling for cities using Cloud computing, J. Cloud Comput., 2, 1–14, 2013. a
Goodfellow, I.: Nips 2016 tutorial: Generative adversarial networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1701.00160, 2016. a
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair,
S., Courville, A., and Bengio, Y.: Generative adversarial nets, Adv. Neural Info. Proc. Syst., 27, https://doi.org/10.48550/arXiv.1406.2661, 2014. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press,
http://www.deeplearningbook.org (last access: 8 August 2022), 2016. a
Hajij, M., Istvan, K., and Zamzmi, G.: Cell complex neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.00743, 2020. a
Hess, L., Melack, J., Filoso, S., and Wang, Y.: Delineation of inundated area
and vegetation along the Amazon floodplain with the SIR-C synthetic aperture
radar, IEEE T. Geosci. Remote, 33, 896–904,
https://doi.org/10.1109/36.406675, 1995. a
Hofmann, J. and Schüttrumpf, H.: floodGAN: Using Deep Adversarial Learning
to Predict Pluvial Flooding in Real Time, Water, 13, https://doi.org/10.3390/w13162255, 2021. a
Horritt, M. S. and Bates, P. D.: Evaluation of 1D and 2D numerical models for
predicting river flood inundation, J. Hydrol., 268, 87–99,
https://doi.org/10.1016/S0022-1694(02)00121-X, 2002. a
Hosseiny, H.: A deep learning model for predicting river flood depth and
extent, Environ. Modell. Softw., 145, 105186, https://doi.org/10.1016/j.envsoft.2021.105186, 2021. a, b, c
Huang, P. C., Hsu, K. L., and Lee, K. T.: Improvement of Two-Dimensional
Flow-Depth Prediction Based on Neural Network Models By Preprocessing
Hydrological and Geomorphological Data, Water Resour. Manage., 35, 1079–1100, https://doi.org/10.1007/s11269-021-02776-9, 2021b. a, b
Ireland, G., Volpi, M., and Petropoulos, G. P.: Examining the Capability of
Supervised Machine Learning Classifiers in Extracting Flooded Areas from
Landsat TM Imagery: A Case Study from a Mediterranean Flood, Remote Sens.,
7, 3372–3399, https://doi.org/10.3390/rs70303372, 2015. a
Isufi, E., Gama, F., and Ribeiro, A.: EdgeNets: Edge varying graph neural
networks, IEEE T. Pattern Anal., https://doi.org/10.1109/TPAMI.2021.3111054,
2021. a
Jacquier, P., Abdedou, A., Delmas, V., and Soulaïmani, A.: Non-intrusive
reduced-order modeling using uncertainty-aware Deep Neural Networks and
Proper Orthogonal Decomposition: Application to flood modeling, J. Comput. Phys., 424, 109854, https://doi.org/10.1016/j.jcp.2020.109854, 2021. a, b, c, d, e, f, g
Jafari, N., Li, X., Chen, Q., Le, C.-Y., Betzer, L., and Liang, Y.: Real-time
water level monitoring using live cameras and computer vision techniques,
Comput. Geosci., 147, https://doi.org/10.1016/j.cageo.2020.104642, 2021. a
Jiang, P., Meinert, N., Jordão, H., Weisser, C., Holgate, S., Lavin, A.,
Lütjens, B., Newman, D., Wainwright, H., Walker, C., and Barnard, P.: Digital Twin
Earth–Coasts: Developing a fast and physics-informed surrogate model for
coastal floods via neural operators, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.07100, 2021. a
Jonkman, S. and Vrijling, J.: Loss of life due to floods, J. Flood
Risk Manage., 1, 43–56, https://doi.org/10.1111/j.1753-318x.2008.00006.x, 2008. a
Kang, W., Xiang, Y., Wang, F., Wan, L., and You, H.: Flood Detection in
Gaofen-3 SAR Images via Fully Convolutional Networks, Sensors, 18, 2915,
https://doi.org/10.3390/s18092915, 2018. a, b, c
Kalos, M. H. and Whitlock, P. A.: Monte carlo methods. John Wiley & Sons, 2009. a
Kazakis, N., Kougias, I., and Patsialis, T.: Assessment of flood hazard areas
at a regional scale using an index-based approach and Analytical Hierarchy
Process: Application in Rhodope-Evros region, Greece, Sci. Total
Environ., 538, 555–563,
https://doi.org/10.1016/j.scitotenv.2015.08.055, 2015. a
Kingma, D. P. and Welling, M.: Auto-encoding variational bayes, arXiv [preprint], https://doi.org/10.48550/arXiv.1312.6114, 2013. a
Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart,
A., and Anandkumar, A.: Neural operator: Learning maps between function
spaces, arXiv [preprint], https://doi.org/10.48550/arXiv.2108.08481, 2021. a, b
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.:
NeuralHydrology – Interpreting LSTMs in Hydrology, Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 347–362,
https://doi.org/10.1007/978-3-030-28954-6_19, 2019a. a
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and
Nearing, G. S.: Toward Improved Predictions in Ungauged Basins: Exploiting
the Power of Machine Learning, Water Resour. Res., 55,
11344–11354, https://doi.org/10.1029/2019WR026065, 2019b. a
Kummu, M., De Moel, H., Ward, P. J., and Varis, O.: How close do we live to
water? A global analysis of population distance to freshwater bodies, PloS
One, 6, e20578, https://doi.org/10.1371/journal.pone.0020578, 2011. a
LeCun, Y. and Bengio, Y.: Convolutional Networks for Images, Speech and Time Series , The MIT Press , 255–258, 1995. a
Lei, X., Chen, W., Panahi, M., Falah, F., Rahmati, O., Uuemaa, E., Kalantari,
Z., Ferreira, C. S. S., Rezaie, F., Tiefenbacher, J. P. and Lee, S.: Urban flood
modeling using deep-learning approaches in Seoul, South Korea, J. Hydrol., 601, 126684, https://doi.org/10.1016/j.jhydrol.2021.126684,2021. a, b, c, d, e
Lendering, K., Jonkman, S., and Kok, M.: Effectiveness of emergency measures
for flood prevention, J. Flood Risk Manage., 9, 320–334, 2016. a
Li, L., Chen, Y., Xu, T., Liu, R., Shi, K., and Huang, C.: Super-resolution
mapping of wetland inundation from remote sensing imagery based on
integration of back-propagation neural network and genetic algorithm, Remote
Sens. Environ., 164, 142–154,
https://doi.org/10.1016/j.rse.2015.04.009, 2015. a, b, c, d, e, f
Li, L., Chen, Y., Xu, T., Huang, C., Liu, R., and Shi, K.: Integration of
Bayesian regulation back-propagation neural network and particle swarm
optimization for enhancing sub-pixel mapping of flood inundation in river
basins, Remote Sens. Lett., 7, 631–640,
https://doi.org/10.1080/2150704X.2016.1177238, 2016a. a, b, c, d
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart,
A., and Anandkumar, A.: Neural operator: Graph kernel network for partial
differential equations, arXiv [preprint], https://doi.org/10.48550/arXiv.2003.03485, 2020. a
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart,
A., and Anandkumar, A.: Fourier Neural Operator for Parametric Partial
Differential Equations, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.08895, 2021. a
Lin, L., Di, L., Yu, E. G., Kang, L., Shrestha, R., Rahman, M. S., Tang, J.,
Deng, M., Sun, Z., Zhang, C., et al.: A review of remote sensing in flood
assessment, in: 2016 Fifth International Conference on Agro-Geoinformatics
(Agro-Geoinformatics), IEEE, 1–4, 2016. a
Lin, Q., Leandro, J., Wu, W., Bhola, P., and Disse, M.: Prediction of Maximum
Flood Inundation Extents With Resilient Backpropagation Neural Network: Case
Study of Kulmbach, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00332, 2020b. a, b, c, d
Lino, M., Cantwell, C., Bharath, A. A., and Fotiadis, S.: Simulating Continuum
Mechanics with Multi-Scale Graph Neural Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2106.04900, 2021. a
Liu, J., Wang, J., Xiong, J., Cheng, W., Sun, H., Yong, Z., and Wang, N.:
Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods
for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets,
Remote Sens., 13, 4945, https://doi.org/10.3390/rs13234945, 2021. a, b, c, d, e
Lu, L., Jin, P., and Karniadakis, G. E.: DeepONet: Learning nonlinear operators
for identifying differential equations based on the universal approximation
theorem of operators, CoRR, abs/1910.03193,
http://arxiv.org/abs/1910.03193 (last access date: 18 August 2022), 2019. a
Lütjens, B., Leshchinskiy, B., Requena-Mesa, C., Chishtie, F.,
Díaz-Rodriguez, N., Boulais, O., Piña, A., Newman, D., Lavin, A.,
Gal, Y., et al.: Physics-informed gans for coastal flood visualization, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.08103, 2020. a, b
Lütjens, B., Leshchinskiy, B., Requena-Mesa, C., Chishtie, F.,
Díaz-Rodríguez, N., Boulais, O., Sankaranarayanan, A., Piña,
A., Gal, Y., Raïssi, C., et al.: Physically-Consistent Generative
Adversarial Networks for Coastal Flood Visualization, arXiv [preprint], https://doi.org/10.48550/arXiv.2104.04785, 2021. a, b
Ma, X., Hong, Y., Song, Y., and Chen, Y.: A super-resolution
convolutional-neural-network-based approach for subpixel mapping of
hyperspectral images, IEEE J. Sel. Top. Appl., 12, 4930–4939, 2019. a
Mahesh, R. B., Leandro, J., and Lin, Q.: Physics Informed Neural Network for
Spatial-Temporal Flood Forecasting, in: Climate Change and Water Security,
edited by Kolathayar, S., Mondal, A., and Chian, S. C., Springer
Singapore, Singapore, 77–91, 2022. a
Mahmoud, S. H. and Gan, T. Y.: Multi-criteria approach to develop flood
susceptibility maps in arid regions of Middle East, J. Cleaner
Prod., 196, 216–229,
https://doi.org/10.1016/j.jclepro.2018.06.047, 2018. a
Manavalan, R.: SAR image analysis techniques for flood area mapping-literature
survey, Earth Sci. Inf., 10, 1–14, 2017. a
Manjusree, P., Kumar, L. P., Bhatt, C. M., Rao, G. S., and Bhanumurthy, V.:
Optimization of threshold ranges for rapid flood inundation mapping by
evaluating backscatter profiles of high incidence angle SAR images,
Int. J. Dis. Risk Sci., 3, 113–122, 2012. a
Mao, Z., Jagtap, A. D., and Karniadakis, G. E.: Physics-informed neural
networks for high-speed flows, Comput. Meth. Appl. Mech. Eng., 360, 112789, https://doi.org/10.1016/j.cma.2019.112789,
2020. a
Martinis, S., Twele, A., and Voigt, S.: Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data, Nat. Hazards Earth Syst. Sci., 9, 303–314, https://doi.org/10.5194/nhess-9-303-2009, 2009. a
Tabari, H.: Climate change impact on flood and extreme precipitation increases with water availability, Sci. Rep., 10, 1–10, 2020. a
Mavriplis, D.: Unstructured grid techniques, Annu. Rev. Fluid Mech.,
29, 473–514, 1997. a
Meraner, A., Ebel, P., Zhu, X. X., and Schmitt, M.: Cloud removal in Sentinel-2
imagery using a deep residual neural network and SAR-optical data fusion,
ISPRS J. Photo. Remote Sens., 166, 333–346, 2020. a
Ming, X., Liang, Q., Xia, X., Li, D., and Fowler, H. J.: Real-Time Flood
Forecasting Based on a High-Performance 2-D Hydrodynamic Model and Numerical
Weather Predictions, Water Resour. Res., 56, e2019WR025583,
https://doi.org/10.1029/2019WR025583, 2020. a
Mitchell, T. M.: Machine Learning, McGraw-Hill, 1997. a
Mosavi, A., Ozturk, P., and Chau, K.-W.: Flood Prediction Using Machine
Learning Models: Literature Review, Water, 10, 1536, https://doi.org/10.3390/w10111536, 2018. a
Moy de Vitry, M., Kramer, S., Wegner, J. D., and Leitão, J. P.: Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network, Hydrol. Earth Syst. Sci., 23, 4621–4634, https://doi.org/10.5194/hess-23-4621-2019, 2019. a
Neumann, B., Vafeidis, A. T., Zimmermann, J., and Nicholls, R. J.: Future
coastal population growth and exposure to sea-level rise and coastal
flooding-a global assessment, PloS One, 10, e0118571, https://doi.org/10.1371/journal.pone.0118571, 2015. a
Ngo, P. T. T., Hoang, N. D., Pradhan, B., Nguyen, Q. K., Tran, X. T., Nguyen,
Q. M., Nguyen, V. N., Samui, P., and Bui, D. T.: A novel hybrid swarm
optimized multilayer neural network for spatial prediction of flash floods in
tropical areas using sentinel-1 SAR imagery and geospatial data, Sensors, 18, 3704, https://doi.org/10.3390/s18113704, 2018. a, b, c, d, e
Nogueira, K., Fadel, S. G., Dourado, I. C., De O. Werneck, R., Muñoz,
J. A., Penatti, O. A., Calumby, R. T., Li, L. T., Dos Santos, J. A., and
Da S. Torres, R.: “Exploiting ConvNet Diversity for Flooding Identification”, in IEEE Geoscience and Remote Sensing Letters, Vol. 15, no. 9, 1446–1450, Sept. 2018, https://doi.org/10.1109/LGRS.2018.2845549, 2017. a, b, c, d, e
Observatory, D. F.: Space-based Measurement, Mapping, and Modeling of Surface
Water, https://floodobservatory.colorado.edu/, last access: 11 November 2021. a
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A.,
Kalchbrenner, N., Senior, A., and Kavukcuoglu, K.: Wavenet: A generative
model for raw audio, arXiv [preprint], https://doi.org/10.48550/arXiv.1609.03499, 2016. a
Papaioannou, G., Vasiliades, L., Loukas, A., and Aronica, G. T.: Probabilistic
flood inundation mapping at ungauged streams due to roughness coefficient
uncertainty in hydraulic modelling, Adv. Geosci., 44, 23–34,
2017. a
Pereira, J., Monteiro, J., Silva, J., Estima, J., and Martins, B.: Assessing
flood severity from crowdsourced social media photos with deep neural
networks, Multimedia Tools Appl., 79, 26197–26223, https://doi.org/10.1007/s11042-020-09196-8,
2020. a
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W.: Learning
Mesh-Based Simulation with Graph Networks, International Conference on
Learning Representations (ICLR), https://doi.org/10.48550/arXiv.2010.03409, 2020. a
Pham, B. T., Luu, C., Van Phong, T., Trinh, P. T., Shirzadi, A., Renoud, S.,
Asadi, S., Van Le, H., von Meding, J., and Clague, J. J.: Can deep learning
algorithms outperform benchmark machine learning algorithms in flood
susceptibility modeling?, J. Hydrol., 592, 125615, https://doi.org/10.1016/j.jhydrol.2020.125615, 2021. a
Prestininzi, P.: Suitability of the diffusive model for dam break simulation:
Application to a CADAM experiment, J. Hydrol., 361, 172–185, 2008. a
Raissi, M., Perdikaris, P., and Karniadakis, G. E.: Physics-informed neural
networks: A deep learning framework for solving forward and inverse problems
involving nonlinear partial differential equations, J. Comput. Phys., 378, 686–707, https://doi.org/10.1016/j.jcp.2018.10.045, 2019. a
Rasmussen, C. E.: Gaussian processes in machine learning, in: Summer school on
machine learning, Springer, 63–71, 2003. a
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P.,
Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al.: Skillful
Precipitation Nowcasting using Deep Generative Models of Radar, arXiv [preprint], https://doi.org/10.48550/arXiv.2104.00954, 2021. a, b
Rossi, C., Acerbo, F., Ylinen, K., Juga, I., Nurmi, P., Bosca, A., Tarasconi,
F., Cristoforetti, M., and Alikadic, A.: Early detection and information
extraction for weather-induced floods using social media streams,
Int. J. Dis. Risk Reduct., 30, 145–157,
https://doi.org/10.1016/j.ijdrr.2018.03.002, 2018. a
Saeed, M., Li, H., Ullah, S., Rahman, A.-u., Ali, A., Khan, R., Hassan, W.,
Munir, I., and Alam, S.: Flood Hazard Zonation Using an Artificial Neural
Network Model: A Case Study of Kabul River Basin, Pakistan, Sustainability,
13, 13953, https://doi.org/10.3390/su132413953, 2021. a, b
Schmidt, V., Luccioni, A., Mukkavilli, S. K., Balasooriya, N., Sankaran, K.,
Chayes, J., and Bengio, Y.: Visualizing the consequences of climate change
using cycle-consistent adversarial networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1905.03709,
2019. a
Serinaldi, F., Loecker, F., Kilsby, C. G., and Bast, H.: Flood propagation and
duration in large river basins: a data-driven analysis for reinsurance
purposes, Nat. Hazards, 94, 71–92, https://doi.org/10.1007/s11069-018-3374-0,
2018. a
Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., and Woo, W. C.:
Convolutional LSTM network: A machine learning approach for precipitation
nowcasting, Adv. Neural Info. Proc. Syst., 2015,
802–810, 2015. a
Shirzadi, A., Asadi, S., Shahabi, H., Ronoud, S., Clague, J. J., Khosravi, K.,
Pham, B. T., Ahmad, B. B., and Bui, D. T.: A novel ensemble learning based on
Bayesian Belief Network coupled with an extreme learning machine for flash
flood susceptibility mapping, Eng. Appl. Art. Intel., 96, 103971, https://doi.org/10.1016/j.engappai.2020.103971, 2020. a
Sikorska, A. E., Viviroli, D., and Seibert, J.: Flood-type classification in
mountainous catchments using crisp and fuzzy decision trees, J Am. Water Resour. Assoc., 5, 2–2,
https://doi.org/10.1111/j.1752-1688.1969.tb04897.x, 2015. a
Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y., and Demir, I.: A
comprehensive review of deep learning applications in hydrology and water
resources, Water Sci. Technol., 82, 2635–2670,
https://doi.org/10.2166/wst.2020.369, 2020. a, b
Sridharan, B., Bates, P. D., Sen, D., and Kuiry, S. N.: Local-inertial shallow
water model on unstructured triangular grids, Adv. Water Res.,
152, 103930, https://doi.org/10.1016/j.advwatres.2021.103930, 2021. a
Taormina, R. and Galelli, S.: Deep-learning approach to the detection and
localization of cyber-physical attacks on water distribution systems, J. Water Res. Plan. Manage., 144, 04018065, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000983, 2018. a
Tehrany, M. S., Lee, M.-J., Pradhan, B., Jebur, M. N., and Lee, S.: Flood
susceptibility mapping using integrated bivariate and multivariate
statistical models, Environ. Earth Sci., 72, 4001–4015, 2014. a
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F., Dutta, D., and Kim, S.:
Flood inundation modelling: A review of methods, recent advances and
uncertainty analysis, Environ. Modell. Softw., 90, 201–216,
https://doi.org/10.1016/j.envsoft.2017.01.006, 2017. a
Tien, D., Hoang, N.-D., Martínez-álvarez, F., Ngo, P.-T. T., Viet,
P., Dat, T., Samui, P., and Costache, R.: A novel deep learning neural
network approach for predicting flash flood susceptibility: A case study at
a high frequency tropical storm area, Sci. Total Environ., 701,
134413, https://doi.org/10.1016/j.scitotenv.2019.134413, 2020. a, b, c, d, e
van de Giesen, N., Hut, R., and Selker, J.: The trans-African
hydro-meteorological observatory (TAHMO), Wiley Interdisciplinary Reviews,
Water, 1, 341–348, 2014. a
Vandaele, R., Dance, S. L., and Ojha, V.: Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning, Hydrol. Earth Syst. Sci., 25, 4435–4453, https://doi.org/10.5194/hess-25-4435-2021, 2021. a
Vandenberg-Rodes, A., Moftakhari, H. R., AghaKouchak, A., Shahbaba, B.,
Sanders, B. F., and Matthew, R. A.: Projecting nuisance flooding in a warming
climate using generalized linear models and Gaussian processes, J. Geophys. Res.-Oceans, 121, 8008–8020,
https://doi.org/10.1002/2016JC012084, 2016. a
Wang, R., Walters, R., and Yu, R.: Incorporating symmetry into deep dynamics
models for improved generalization, arXiv [preprint], https://doi.org/10.48550/arXiv.2002.03061,
2020. a
Wardhani, N. W. S., Rochayani, M. Y., Iriany, A., Sulistyono, A. D., and
Lestantyo, P.: Cross-validation metrics for evaluating classification
performance on imbalanced data, in: 2019 international conference on
computer, control, informatics and its applications (ic3ina),
IEEE, 14–18, 2019. a
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S.: A Comprehensive
Survey on Graph Neural Networks, IEEE T. Neur. Net. Lear., 32, 4–24, https://doi.org/10.1109/TNNLS.2020.2978386, 2021. a, b
Xie, S., Wu, W., Mooser, S., Wang, Q., Nathan, R., and Huang, Y.: Artificial
neural network based hybrid modeling approach for flood inundation modeling,
J. Hydrol., 592, 125605, https://doi.org/10.1016/j.jhydrol.2020.125605, 2021. a, b
Yakti, B. P., Adityawan, M. B., Farid, M., Suryadi, Y., Nugroho, J., and
Hadihardaja, I. K.: 2D modeling of flood propagation due to the failure of
way Ela natural dam, in: MATEC Web of Conferences, Vol. 147, EDP Sciences,
https://doi.org/10.1051/matecconf/201814703009, 2018. a
Yang, M., Isufi, E., and Leus, G.: Simplicial Convolutional Neural Networks, ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 8847–8851, https://doi.org/10.1109/ICASSP43922.2022.9746017, 2022. a
Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.-H., and Liao, Q.: Deep
learning for single image super-resolution: A brief review, IEEE T. Multimedia, 21, 3106–3121, 2019. a
Yang, X. I. A., Zafar, S., Wang, J.-X., and Xiao, H.: Predictive
large-eddy-simulation wall modeling via physics-informed neural networks,
Phys. Rev. Fluids, 4, 034602, https://doi.org/10.1103/PhysRevFluids.4.034602,
2019. a
Yokoya, N., Yamanoi, K., He, W., Baier, G., Adriano, B., Miura, H., and Oishi,
S.: Breaking limits of remote sensing by deep learning from simulated data
for flood and debris-flow mapping, IEEE T. Geosci. Remote, https://doi.org/10.1109/TGRS.2020.3035469, 2020. a, b, c
Youssef, A. M., Pradhan, B., and Sefry, S. A.: Flash flood susceptibility
assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and
multivariate statistical models, Environ. Earth Sci., 75, 1–16, https://doi.org/10.1007/s12665-015-4830-8, 2016. a
Zhang, S., Xia, Z., Yuan, R., and Jiang, X.: Parallel computation of a
dam-break flow model using OpenMP on a multi-core computer, J. Hydrol., 512, 126–133, 2014. a
Zhang, Z., Flora, K., Kang, S., Limaye, A. B., and Khosronejad, A.: Data-driven
prediction of turbulent flow statistics past bridge piers in large-scale
rivers using convolutional neural networks, Water Resour. Res., 58, e2021WR030163, https://doi.org/10.1029/2021WR030163, 2021. a
Zhao, G., Bates, P., Neal, J., and Pang, B.: Design flood estimation for global river networks based on machine learning models, Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, 2021a. a
Zhao, G., Balstrøm, T., Mark, O., and Jensen, M. B.: Multi-Scale
Target-Specified Sub-Model Approach for Fast Large-Scale High-Resolution 2D
Urban Flood Modelling, Water, 13, 259, https://doi.org/10.3390/w13030259,
2021b. a
Zhao, G., Pang, B., Xu, Z., Cui, L., Wang, J., Zuo, D., and Peng, D.: Improving
urban flood susceptibility mapping using transfer learning, J. Hydrol., 602, 126777,
https://doi.org/10.1016/j.jhydrol.2021.126777, 2021c. a, b, c
Zhou, Y., Wu, C., Li, Z., Cao, C., Ye, Y., Saragih, J., Li, H., and Sheikh, Y.:
Fully convolutional mesh autoencoder using efficient spatially varying kernels. Advances in Neural Information Processing Systems, 33, 9251–9262, 2020.
Zhou, Y., Wu, C., Li, Z., Cao, C., Ye, Y., Saragih, J., Li, H. and Sheikh, Y., 2020. Fully convolutional mesh autoencoder using efficient spatially varying kernels. Advances in Neural Information Processing Systems, 33, pp.9251-9262. a
Zounemat-Kermani, M., Matta, E., Cominola, A., Xia, X., Zhang, Q., Liang, Q.,
and Hinkelmann, R.: Neurocomputing in surface water hydrology and hydraulics:
A review of two decades retrospective, current status and future prospects,
J. Hydrol., 588, 125085,
https://doi.org/10.1016/j.jhydrol.2020.125085, 2020. a
Short summary
Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
Deep learning methods have been increasingly used in flood management to improve traditional...
