U-FLOOD – Topographic deep learning for predicting urban pluvial flood water depth

Löwe, Roland; Bohm, J.; Jensen, David G.; Leandro, Jorge; Rasmussen, Søren Højmark

doi:10.1016/j.jhydrol.2021.126898

Cited by 91 publications

(123 citation statements)

References 44 publications

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“…The scientific community has dedicated significant efforts to exploring the potential of DL for urban flooding. The latter is more difficult to model because of the complex topography and the presence of a drainage system whose dynamics need to be coupled with the overland flood (Löwe et al, 2021). All papers analyzing flash floods described flood susceptibility mapping applications.…”

Section: Flood Typesmentioning

confidence: 99%

“…The cases sizes vary from very small ones, 10000m 2 (Gebrehiwot et al, 2019), to small towns up to 100km 2 (Lin et al, 2020a). Regional scale models consider a catchment (e.g., Popa et al, 2019), a province (e.g., Wang et al, 2020) or large cities (e.g., Löwe et al, 2021;Kalantar et Africa and Asia, and Australia, respectively, but since the size of each area were smaller than 50000km 2 they were marked as regional scale models. They also do not fit within the national scale classification since they do not encompass whole nations.…”

Section: Spatial Scalementioning

confidence: 99%

“…In particular, RNNs are applied when a spatial-temporal estimation of the water depths is performed. CNNs were initially discarded but are used more in recent years (Guo et al, 2020;Löwe et al, 2021;Kabir et al, 2020).…”

Section: Architecturementioning

confidence: 99%

“…However, this transference between areas is challenging for DL models because of the difference in input and output data. Löwe et al (2021) generate flood hazard maps also for areas that were not considered during training. These areas were within the same study region, thus there was little variability of inputs and outputs with respect to the training set.…”

Section: Modeling Limitationsmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Bentivoglio

Isufi

Jonkman

et al. 2021

Preprint

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Abstract. Deep Learning techniques have been increasingly used in flood risk management to overcome the limitations of accurate, yet slow, numerical models, and to improve the results of traditional methods for flood mapping. In this paper, we review 45 recent publications to outline the state-of-the-art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate as they leverage inductive biases to better process the spatial characteristics of the flooding events. Traditional models based on fully-connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist real-time flood warning during an emergency, and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies and sites. Furthermore, all reviewed models and their outputs, are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Neural operators can also speed up numerical models while preserving the underlying physical equations and could thus be used for reliable real-time warning. Similarly, probabilistic models can be built by resorting to Deep Gaussian Processes.

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Section: Flood Typesmentioning

confidence: 99%

Section: Spatial Scalementioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

Section: Modeling Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Bentivoglio

Isufi

Jonkman

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The scientific community has dedicated significant efforts to exploring the potential of DL for urban flooding. This is difficult to model because of the complex topography and the presence of a drainage system whose dynamics need to be coupled with the overland flood (Löwe et al, 2021). Almost all papers analyzing flash floods described flood susceptibility mapping applications.…”

Section: Flood Typesmentioning

confidence: 99%

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Bentivoglio

Isufi

Jonkman

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Deep Learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models, and to improve the results of traditional methods for flood mapping. In this paper, we review 58 recent publications to outline the state-of-the-art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate as they leverage inductive biases to better process the spatial characteristics of the flooding events. Models based on fully-connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist real-time flood warning during an emergency, and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies. Furthermore, all reviewed models and their outputs, are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Physics-based deep learning can be used to preserve the underlying physical equations resulting in more reliable speed-up alternatives for numerical models. Similarly, probabilistic models can be built by resorting to Deep Gaussian Processes or Bayesian neural networks.

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Monthly precipitation prediction at regional scale using deep convolutional neural networks

Ni,

Wang,

Singh

et al. 2023

Hydrological Processes

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Variations in monthly precipitation are associated with climate extremes having significant socio‐economic and eco‐environmental impacts. Knowledge of monthly precipitation information is therefore valuable for policy making. Extensive research has been conducted on dynamic prediction using state‐of‐the‐art coupled climate models. However, the skilful prediction of monthly precipitation with dynamical models remains a challenge. With the development of machine learning tools, statistical predictions show comparable performance with dynamic models, but they are limited to at‐site monthly prediction, due to the lack of ability for processing spatially connected geophysical data. To improve monthly precipitation forecasting and provide regional forecasts, we propose a model termed UNet‐RegPre based on convolutional neural network and U‐net architecture. The model shows comparable prediction skills with recurrent state‐of‐the‐art dynamic forecasts (CFSv2), and has the capability to capture spatiotemporal patterns of precipitation and reproduce the main process of representative droughts. The key precursors for rainfall development identified by UNet‐RegPre shows that the constructed model can detect the climatic connection between rainfall in eastern China and summer monsoon, showing the potential to advance hydrometeorological understanding with a deep learning‐based model. These results suggest that the proposed model can be a potential tool for precipitation prediction.

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U-FLOOD – Topographic deep learning for predicting urban pluvial flood water depth

Cited by 91 publications

References 44 publications

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Monthly precipitation prediction at regional scale using deep convolutional neural networks

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