Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping

Annis, Antonio; Nardi, Fernando

doi:10.1080/10095020.2019.1626135

Cited by 56 publications

(34 citation statements)

References 75 publications

(68 reference statements)

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“…Therefore, monitoring the flooded area of Chaohu Lake is of great significance. Due to the overcast during flood events, optical remote sensing sensors often fail to obtain effective data (Annis and Nardi 2019). As an active detector, SAR sensors are able to penetrate cloud cover, haze, dust and overcome other climatic conditions thanks to its long wavelength, which is not easily affected by meteorological conditions and sunshine level.…”

Section: Urban Temporal-spectral Observation Modelmentioning

confidence: 99%

Spatio-temporal-spectral observation model for urban remote sensing

Shao

2021

Geo-spatial Information Science

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Taking cities as objects being observed, urban remote sensing is an important branch of remote sensing. Given the complexity of the urban scenes, urban remote sensing observation requires data with a high temporal resolution, high spatial resolution, and high spectral resolution. To the best of our knowledge, however, no satellite owns all the above characteristics. Thus, it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation. In this study, we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model, filling the gap of no existing urban remote sensing framework. In this study, we present four applications to elaborate on the specific applications of the proposed model: 1) a spatiotemporal fusion model for synthesizing ideal data, 2) a spatio-spectral observation model for urban vegetation biomass estimation, 3) a temporal-spectral observation model for urban flood mapping, and 4) a spatio-temporal-spectral model for impervious surface extraction. We believe that the proposed model, although in a conceptual stage, can largely benefit urban observation by providing a new data fusion paradigm.

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Section: Urban Temporal-spectral Observation Modelmentioning

confidence: 99%

Spatio-temporal-spectral observation model for urban remote sensing

Shao

2021

Geo-spatial Information Science

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“…As reported by Esposito et al [61], many studies have underlined the valuable use of web news and social media for flood mapping [62], streamflow estimation [63,64], damage assessment [65], and flood prediction [66,67]. Crowdsourced data and Volunteered Geographic Information (VGI) have also been used to develop road damage maps, representing a fundamental tool in disaster response operations [68].…”

Section: Evaluation Of Flooding Impact Using Crowdsourced Datamentioning

confidence: 99%

Characterizing Consecutive Flooding Events after the 2017 Mt. Salto Wildfires (Southern Italy): Hazard and Emergency Management Implications

Esposito¹,

Parodi²,

Lagasio³

et al. 2019

Water

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Every summer, wildfires affect thousands of steep watersheds in Italy, causing the partial or complete destruction of vegetation, and changes in soil hydraulic properties. Such effects alter the hydrologic response of watersheds, increasing post-fire debris and sediment-laden flow hazard. This study characterizes the most relevant predisposing and triggering factors for a sequence of four post-fire flooding events, which, in the late summer-autumn of 2017, affected Montoro village in southern Italy. This research work consists of a fire severity assessment based on multispectral satellite images, characterization of meteorological systems and related flood-triggering rainfall, and provides an overview of the damage that occurred in the repeatedly affected urban area using crowdsourced data. The research findings demonstrate that the analyzed area burned with moderate-high (64.4%) and low severity (35.6%) levels. All the flooding events were triggered by rainfall evaluated as non-extreme, but with relevant peak intensities (I10 and I30), associated with the first convective storms impacting the burned watersheds. The crowdsourced data highlight the fact that roads and buildings on footslopes were inundated by mud and debris transported by rapid flows. The study identifies a clear relationship between wildfires and flooding processes and provides useful information for hazard assessment and emergency management operations.

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“…During a flood event, affected populations frequently produce "user-generated content" or "crowd-sourced" data from social media posts or apps where citizens can report floods (Mazoleni et al, 2017;Assumpção et al, 2018;Annis and Nardi, 2019;UrbanRiskLab, 2019). This is especially the case in urban areas, where internet and social media penetration are higher compared to rural areas.…”

Section: Descriptive Hazard Assessmentsmentioning

confidence: 99%

“…Fohringer et al, 2015), citizen science (e.g. Annis and Nardi, 2019) and other sources. This impulse of new data combined with machine algorithms could lead to changes in flood risk and impact assessment.…”

Section: Introductionmentioning

confidence: 99%

Invited perspectives: How machine learning will change flood risk and impact assessment

Wagenaar

Curran

Balbi

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data, are causing changes in almost every aspect of our lives. This trend is expected to continue as more data keep becoming available, computing power keeps improving and machine learning algorithms keep improving as well. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications will become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure and on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other components, machine learning may not always be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully to avoid misuse. This paper presents some of the current developments on the application of machine learning in this field and highlights some key needs and challenges.

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Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping

Cited by 56 publications

References 75 publications

Spatio-temporal-spectral observation model for urban remote sensing

Spatio-temporal-spectral observation model for urban remote sensing

Characterizing Consecutive Flooding Events after the 2017 Mt. Salto Wildfires (Southern Italy): Hazard and Emergency Management Implications

Invited perspectives: How machine learning will change flood risk and impact assessment

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