Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks

Lago, César Ambrogi Ferreira do; Giacomoni, Marcio; Bentivoglio, Roberto; Taormina, Riccardo; Júnior, Marcus Nóbrega Gomes; Mendiondo, Eduardo Mário

doi:10.1016/j.jhydrol.2023.129276

Cited by 27 publications

(10 citation statements)

References 31 publications

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“…GANs can have several different applications in flood management. Some of these applications include flood damage assessment, flood forecasting, flood early warning systems, and flood risk assessment [116]. GANs can be used to create synthetic flood data for model training and testing in the context of flood management [117].…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

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Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In an attempt to explore transfer learning for the same task, Oruche et al (2021) trained LSTMs in which the network is first trained over data-rich regions and then fine-tuned over data-sparse regions. In a quite different methodology that aims to generate matrices instead of forecasting time-series, do Lago et al (2023) utilized conditional generative adversarial networks (cGANs) to generate flood plains for certain flood scenarios.…”

Section: Prediction In Ungauged Basinsmentioning

confidence: 99%

Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks

Sit¹,

Demiray²,

Demir³

2023

Preprint

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Accurate streamflow data is vital for various climate modeling applications, including flood forecasting. However, many streams lack sufficient monitoring due to the high operational costs involved. To address this issue and promote enhanced disaster preparedness, management, and response, our study introduces a neural network-based method for estimating historical hourly streamflow in two spatial downscaling scenarios. The method targets two types of ungauged locations: (1) those without sensors in sparsely gauged river networks, and (2) those that previously had a streamflow sensor, but the gauge is no longer available. For both cases, we propose the ScaleGNN, a graph neural network based on Attention-Based Spatio-Temporal Graph Convolutional Networks (ASTGCN). We evaluate the performance of ScaleGNN against a Long Short-Term Memory (LSTM) baseline and spatial persistence in estimating discharge values over a 36-hour period. Our findings indicate that ScaleGNN surpasses spatial persistence in the first scenario, while both neural network approaches demonstrate their effectiveness compared to spatial persistence in the second scenario.

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“…In an attempt to explore transfer learning for the same task, Oruche et al [34] trained LSTMs in which the network is rst trained over datarich regions and then ne-tuned over data-sparse regions. In a quite different methodology that aims to generate matrices instead of forecasting time-series, do Lago et al [35] utilized conditional generative adversarial networks (cGANs) to generate ood plains for certain ood scenarios.…”

Section: Prediction In Ungauged Basinsmentioning

confidence: 99%

Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks

Sit

Demiray

Demir

2023

Preprint

View full text Add to dashboard Cite

Accurate streamflow data is vital for various climate modeling applications, including flood forecasting. However, many streams lack sufficient monitoring due to the high operational costs involved. To address this issue and promote enhanced disaster preparedness, management, and response, our study introduces a neural network-based method for estimating historical hourly streamflow in two spatial downscaling scenarios. The method targets two types of ungauged locations: (1) those without sensors in sparsely gauged river networks, and (2) those that previously had a streamflow sensor, but the gauge is no longer available. For both cases, we propose the ScaleGNN, a graph neural network architecture. We evaluate the performance of ScaleGNN against a Long Short-Term Memory (LSTM) baseline and spatial persistence in estimating discharge values over a 36-hour period. Our findings indicate that ScaleGNN surpasses spatial persistence in the first scenario, while both neural network approaches demonstrate their effectiveness compared to spatial persistence in the second scenario.

show abstract

Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks

Cited by 27 publications

References 31 publications

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks

Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks

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