A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong, Shangjia; Yu, Tianbo; Farahmand, Hamed; Mostafavi, Ali

doi:10.1111/mice.12629

Cited by 31 publications

(38 citation statements)

References 87 publications

(104 reference statements)

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“…Since most data for flood analysis (e.g., elevation data, rainfall distribution fields, remote sensing image) come in this format, CNNs have been increasingly employed by the research community in the recent years. While most papers consider standard CNNs, there are a few which employ 1D-CNNs (e.g., Dong et al, 2021;Guo et al, 2020) and 3D-CNNs (e.g., Wang et al, 2020;Fang et al, 2020a). 1D-CNNs consider as input a hyetograph or a hydrograph of a certain event, while 3D-CNNs consider raster files stacked upon each other.…”

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%

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

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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%

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

“…Since most data for flood analysis (e.g., elevation data, rainfall distribution fields, remote sensing image) come in this format, CNNs have been increasingly employed by the research community in the recent years. While most papers consider standard CNNs, there are a few which employ 1D-CNNs (e.g., Dong et al, 2021;Guo et al, 2021; and 3D-CNNs (e.g., Wang et al, 2020b;Fang et al, 2020a). 1D-CNNs consider as input a hyetograph or a hydrograph of a certain event, while 3D-CNNs consider raster files stacked upon each other.…”

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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“…Currently, ML technology (e.g., regression, classification, and clustering) is being replaced with new ML technologies, such as deep learning (DL) approaches [11]. These approaches primarily include big data, which are high-dimensional datasets in sensor-based measurements [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Urban Drainage Systems: State-of-the-Art Review

Kwon

Kim

2021

Water

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In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies.

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A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Cited by 31 publications

References 87 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

Machine Learning and Urban Drainage Systems: State-of-the-Art Review

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