Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts

Kao, I-Feng; Liou, Jia-Yi; Lee, Meng-Hsin; Chang, Fi‐John

doi:10.1016/j.jhydrol.2021.126371

Cited by 67 publications

(26 citation statements)

References 52 publications

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“…However, they remain the least common choice of DL architecture for spatial flood analysis. Most papers apply RNNs on a time series, such as a hyetograph or a hydrograph (e.g., Kao et al, 2021;Zhou et al, 2021). Some papers, instead, consider spatial sequentiality by reshaping the original raster data into vectors (e.g., Fang et al, 2020a;Panahi et al, 2021).…”

Section: Architecturementioning

confidence: 99%

“…In fact, Panahi et al (2021) shows that these models underperform when compared with CNNs. Among the different RNN layers, most works consider LSTM units (Kao et al, 2021;Zhou et al, 2021;Fang et al, 2020a) but simple recurrent units (Panahi et al, 2021;Huang et al, 2021a) and GRUs (Dong et al, 2021) as their cross-sections, and rainfall and water level measures, taken from sensors in the network. This input is then given in parallel to a 1D-CNN and to a GRU whose output is then combined to predict the temporal evolution of the flood.…”

Section: Architecturementioning

confidence: 99%

“…The inputs are hyetographs, which represent the rainfall precipitation or intensity in time (e.g., Berkhahn et al, 2019;Kao et al, 2021;Guo et al, 2020) or hydrographs, which represent the discharge in time (e.g., Chu et al, 2020;Zhou et al, 2021;Lin et al, 2020a). Other inputs such as the DEM and the roughness coefficient, also used for numerical models, are sometimes considered as additional inputs (e.g., Guo et al, 2020;Chang et al, 2010;Huang et al, 2021b).…”

Section: Input and Output Datamentioning

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

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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: Architecturementioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

Section: Input and Output Datamentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Architecturementioning

confidence: 99%

“…In fact, Panahi et al (2021) and Lei et al (2021) show that these models underperform when compared with CNNs. Among the different RNN layers, most works consider LSTM units (Kao et al, 2021;Zhou et al, 2021;Fang et al, 2020a) but simple recurrent units (Panahi et al, 2021;Huang et al, 2021a) and GRUs (Dong et al, 2021) have also been employed. Some papers analyzed the potential of RNNs in combination with other techniques.…”

Section: Architecturementioning

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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Artificial Neural Network Modelling for Simulating Catchment Runoff: A Case Study of East Melbourne

Balacumaresan,

Aziz,

Choudhury

et al. 2023

Algorithms for Intelligent Systems

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Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts

Cited by 67 publications

References 52 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

Artificial Neural Network Modelling for Simulating Catchment Runoff: A Case Study of East Melbourne

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