Assessment of crowdsourced social media data and numerical modelling as complementary tools for urban flood mitigation

Re, Mariano; Kazimierski, Leandro David; García, Pablo E.; Ortiz, Nicolás; Lagos, Marina

doi:10.1080/02626667.2022.2075266

“…Crowdsourced social media data can be used to improve flood forecasting by providing high spatiotemporal resolution data, especially in urban areas (2). This data can complement traditional observations and be assimilated 560 into flood forecasting models to reduce uncertainties (87). The integration of crowdsourced social media data with urban flood modeling enhances the understanding of flood dynamics and improves model performance (88).…”

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confidence: 99%

Advances in Image-Based Estimation of Snow Hydrology Parameters: A Systematic Literature Review

Demil,

Torabi Haghighi,

Klöve

et al. 2024

Preprint

0

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Abstract. Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are essential for predicting water availability and managing water resources in snow-covered areas. Traditional methods for estimating these parameters often rely on manual measurements or simplistic models, which can be inadequate for capturing the complexity of snow-related processes. In recent years, there has been a growing interest in leveraging deep learning techniques for snow hydrology parameter estimation, offering the potential to overcome these limitations. This review paper comprehensively analyzes the current state, challenges, and future directions of image-based approaches in snow hydrology parameter estimation. By harnessing the power of automated methods, particularly deep learning, these approaches demonstrate the ability to capture intricate spatial and temporal relationships present in image data. A comparative analysis between traditional and image-based methods highlights the strengths of automated approaches, including scalability and accuracy. Integration of image sensors, such as satellite imagery and crowd-sourced data, is explored as a crucial component of snow hydrology parameter estimation. Various satellite image sources, including Sentinel 1-2, Landsat, and MODIS, are discussed in terms of their suitability for snow hydrological applications. Despite the promise of image-based approaches, challenges remain, including data availability, model interpretability, and transferability. The paper identifies future research directions, emphasizing the exploration of novel deep learning architectures and uncertainty quantification techniques to address these challenges. In conclusion, this review underscores the importance of image-based approaches for advancing snow hydrology parameter estimation. By addressing challenges and maximizing potential impact, these approaches have the potential to revolutionize snow hydrological modeling and environmental management.

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“…Crowdsourced social media data can be used to improve flood forecasting by providing high spatiotemporal resolution data, especially in urban areas (2). This data can complement traditional observations and be assimilated 560 into flood forecasting models to reduce uncertainties (87). The integration of crowdsourced social media data with urban flood modeling enhances the understanding of flood dynamics and improves model performance (88).…”

mentioning

confidence: 99%

Advances in Image-Based Estimation of Snow Hydrology Parameters: A Systematic Literature Review

Demil,

Torabi Haghighi,

Klöve

et al. 2024

Preprint

0

View full text Add to dashboard Cite

Abstract. Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are essential for predicting water availability and managing water resources in snow-covered areas. Traditional methods for estimating these parameters often rely on manual measurements or simplistic models, which can be inadequate for capturing the complexity of snow-related processes. In recent years, there has been a growing interest in leveraging deep learning techniques for snow hydrology parameter estimation, offering the potential to overcome these limitations. This review paper comprehensively analyzes the current state, challenges, and future directions of image-based approaches in snow hydrology parameter estimation. By harnessing the power of automated methods, particularly deep learning, these approaches demonstrate the ability to capture intricate spatial and temporal relationships present in image data. A comparative analysis between traditional and image-based methods highlights the strengths of automated approaches, including scalability and accuracy. Integration of image sensors, such as satellite imagery and crowd-sourced data, is explored as a crucial component of snow hydrology parameter estimation. Various satellite image sources, including Sentinel 1-2, Landsat, and MODIS, are discussed in terms of their suitability for snow hydrological applications. Despite the promise of image-based approaches, challenges remain, including data availability, model interpretability, and transferability. The paper identifies future research directions, emphasizing the exploration of novel deep learning architectures and uncertainty quantification techniques to address these challenges. In conclusion, this review underscores the importance of image-based approaches for advancing snow hydrology parameter estimation. By addressing challenges and maximizing potential impact, these approaches have the potential to revolutionize snow hydrological modeling and environmental management.

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“…Although social media data is known for lacking structure, it is equally seen as one of the big data sources that create opportunities for the development of disruptive innovations and advancements in data-driven science (Kitchin, 2014). So far, in the context of flooding, social media data has been previously used for flood water mapping (Fohringer et al, 2015;Rosser et al, 2017), inundation modeling (Guan et al, 2023;Ouyang et al, 2022;Re et al, 2022), providing valuable information for the development of mitigation measures. However, in the context of the previous tropical cyclones that affected South-East Africa, this data has been rarely gathered and organized to direct efforts to mitigate future similar events.…”

mentioning

confidence: 99%

Using social media feeds for mapping and assessing areas affected by flooding due to tropical cyclones

Kalonde,

Chiepa,

Austin

et al. 2023

J Flood Risk Management

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Assessment of crowdsourced social media data and numerical modelling as complementary tools for urban flood mitigation

Cited by 6 publications

References 46 publications

Advances in Image-Based Estimation of Snow Hydrology Parameters: A Systematic Literature Review

Advances in Image-Based Estimation of Snow Hydrology Parameters: A Systematic Literature Review

Advances in Image-Based Estimation of Snow Hydrology Parameters: A Systematic Literature Review

Using social media feeds for mapping and assessing areas affected by flooding due to tropical cyclones

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