From local to regional compound flood mapping with deep learning and data fusion techniques

Muñoz, David F.; Muñoz, Paul; Moftakhari, Hamed; Moradkhani, Hamid

doi:10.1016/j.scitotenv.2021.146927

Cited by 80 publications

(42 citation statements)

References 73 publications

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“…This classification is usually binary (e.g., Peng et al, 2019;Nemni et al, 2020) but it can also be extended to include permanent water bodies (Sarker et al, 2019) (see the example Fig. 1b), vegetation (Ichim and Popescu, 2020), buildings (Hashemi-Beni and Gebrehiwot, 2021), and more (Muñoz et al, 2021). All the types of floods were well represented for this application but flash floods (Fig.…”

Section: Deep Learning For Flood Inundationmentioning

confidence: 99%

“…Satellite data is the most used input for flood inundation applications (e.g., Sarker et al, 2019;Peng et al, 2019;Nogueira et al, 2017). Other input data sources include unmanned aerial vehicles data (UAV) (e.g., Gebrehiwot et al, 2019;Ichim and Popescu, 2020), hydrographs (e.g., Hou et al, 2021) and DEMs (e.g., Hashemi-Beni and Gebrehiwot, 2021;Muñoz et al, 2021). Inundation maps produced by 3D numerical models are also used as target prediction (Muñoz et al, 2021) remote sensing data that represent a flood event seen from above.…”

Section: Input and Output Datamentioning

confidence: 99%

“…Other input data sources include unmanned aerial vehicles data (UAV) (e.g., Gebrehiwot et al, 2019;Ichim and Popescu, 2020), hydrographs (e.g., Hou et al, 2021) and DEMs (e.g., Hashemi-Beni and Gebrehiwot, 2021;Muñoz et al, 2021). Inundation maps produced by 3D numerical models are also used as target prediction (Muñoz et al, 2021) remote sensing data that represent a flood event seen from above. The main differences concern the scale, the resolution, and the availability.…”

Section: Input and Output Datamentioning

confidence: 99%

“…This approach was used by Zhao et al (2021b) for flood susceptibility mapping showing that a pre-trained model improves the mapping's accuracy for data-scarce areas. Muñoz et al (2021) also used it and demonstrated that an inundation model trained on a local scale can be applied to broader scales. Thus, transfer learning carries great potential to bridge the data scarcity gap also in other scenarios.…”

Section: Data Scarcitymentioning

confidence: 99%

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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: Deep Learning For Flood Inundationmentioning

confidence: 99%

Section: Input and Output Datamentioning

confidence: 99%

Section: Input and Output Datamentioning

confidence: 99%

Section: Data Scarcitymentioning

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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“…Using the model, they can re-assess their compound flood events and predict the future events. Moreover, once they have more observation data, they can fuse the data to re-adjust the proposed model or to build a more robust one (Muñoz et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta

Sampurno

Vallaeys

Ardianto³

et al. 2022

Preprint

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Abstract. Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.

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Solving flood problems with deep learning technology: Research status, strategies, and future directions

Li,

Zhu,

et al. 2024

Sustainable Development

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As a frequent and devastating natural disaster worldwide, floods are influenced by complex factors. Building flood models for simulating, monitoring, and forecasting floods is crucial to reduce the risk of disasters and minimize damage to people and property. With advancements in computing power and the impressive capabilities of deep learning in such areas as classification and prediction, there has been growing interest in using this technology in flood research. There is also a growing body of research into building flood data‐driven models with deep learning. Based on this, this study adopts a mixed‐method approach of bibliometric and qualitative analyses to provide an overview of the research. The research status is revealed in a bibliometric visualization, where the research objects are defined from the flood perspective, and the research strategies are explained from the deep learning perspective to provide a comprehensive and in‐depth understanding of the flood problem and how to apply deep learning to solve it. In addition, the study reflects on the future direction of improvement and innovation needed to promote the further development and exploration of deep learning in flood research.

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From local to regional compound flood mapping with deep learning and data fusion techniques

Cited by 80 publications

References 73 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

Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta

Solving flood problems with deep learning technology: Research status, strategies, and future directions

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