Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities

Journal of Hydrology, Год журнала: 2023, Номер 617, С. 129114 - 129114

Опубликована: Янв. 12, 2023

Язык: Английский

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Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis

Journal of Hydrology, Год журнала: 2024, Номер 633, С. 131059 - 131059

Опубликована: Март 8, 2024

Язык: Английский

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Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model

Sustainable Cities and Society, Год журнала: 2024, Номер 108, С. 105508 - 105508

Опубликована: Май 5, 2024

Язык: Английский

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Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui

Applied Sciences, Год журнала: 2025, Номер 15(1), С. 465 - 465

Опубликована: Янв. 6, 2025

Emergency situation awareness during sudden natural disasters presents significant challenges. Traditional methods, characterized by low spatial and temporal resolution as well coarse granularity, often fail to comprehensively capture disaster situations. However, social media platforms, a vital source of sensing, offer potential supplement situational awareness. This paper proposes an innovative framework for based on multimodal data from identify content related typhoon disasters. Integrating text image facilitates near real-time monitoring the public perspective. In this study, Typhoon Haikui (Strong No. 11 2023) was chosen case study validate effectiveness proposed method. We employed ERNIE language processing model complement Deeplab v3+ deep learning semantic segmentation extracting damage information media. A visualization analysis disaster-affected areas performed categorizing types. Additionally, Geodetector used investigate heterogeneity its underlying factors. approach allowed us analyze spatiotemporal patterns evolution, enabling rapid assessment facilitating emergency response efforts. The results show that method significantly enhances effectively identifying different types sensing data.

Язык: Английский

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Building resilient urban drainage systems by integrated flood risk index for evidence-based planning

Journal of Environmental Management, Год журнала: 2025, Номер 374, С. 124130 - 124130

Опубликована: Янв. 14, 2025

Язык: Английский

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Data-driven urban waterlogging risk management approach considering efficiency-equity trade-offs and risk mitigation capability evaluation

Journal of Hydrology, Год журнала: 2024, Номер 634, С. 131004 - 131004

Опубликована: Март 6, 2024

Язык: Английский

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Escalating rainstorm-induced flood risks in the Yellow River Basin, China

Environmental Research Letters, Год журнала: 2024, Номер 19(6), С. 064006 - 064006

Опубликована: Апрель 26, 2024

Abstract The warming climate-induced intensification of hydrological cycle is amplifying extreme precipitation and increasing flood risk at regional global scales. evaluation risk, which depends on assessment indicators, weights, as well data quality, the first step toward mitigation disasters. In this study, we accepted ten indicators concerning hazard disaster-causing factors, sensitivity hazard-forming environments, vulnerability disaster-bearing bodies. We used a combined weighting method based analytic hierarchy process entropy weight (AHP-EW) technique to evaluate rainstorm-induced risks across Yellow River Basin (YRB) from 2000 2018. observed hazards are intensifying YRB. Specifically, areas with medium expanded lower middle upper floods exhibited spatial pattern southeast northwest (lower YRB). increase in vegetation coverage reaches YRB reduces Flood shows an trend, higher mainly overall 9-fold Medium high can be identified YRB, where population gross domestic product concentrated. over urban these regions should arouse public concern.

Язык: Английский

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The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Water, Год журнала: 2024, Номер 16(7), С. 942 - 942

Опубликована: Март 25, 2024

Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light recent advances in knowledge graph theory application technology, their notable integration representation capabilities may offer support dynamic monitoring decision-making processes concerning rainstorm disaster events. However, conventional models do not adequately capture spatiotemporal characteristics these To fill this gap, we analyze essence events divide evolution into four stages, namely, pregnant, development, continuous, decline stages. On basis, multilevel model proposed from layers, which are event, object–state, feature, relationship by analyzing components mechanism The can only express comprehensive structure relationships within events, but also emphasize through series ordered states. Moreover, test utility constructed case study Zhengzhou 720 rainstorm. first validates that selected machine learning extract event accurately comparing with some mainstream models. Then, it demonstrates practical field representation, condition retrieval. Additionally, since show throughout its full life cycle, promote understanding mechanisms pave way future applications prevention reduction.

Язык: Английский

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A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping

Water Resources Management, Год журнала: 2024, Номер 38(15), С. 5823 - 5864

Опубликована: Авг. 3, 2024

Язык: Английский

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Interannual comparison of historical floods through flood detection using multi-temporal Sentinel-1 SAR images, Awash River Basin, Ethiopia

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2023, Номер 124, С. 103505 - 103505

Опубликована: Сен. 28, 2023

Synthetic-aperture radar (SAR) data from Sentinel-1 satellites provides unprecedented opportunity to evaluate inter-annual flood characteristics, although consensus on best detection methods is lacking. This study compared the performance of three characteristics at two sites in Awash River Basin Ethiopia. The are Change Detection and Thresholding (CDAT), Normalized Difference Flood Index (NDFI) Root Image (RNID). reference map was prepared based a field survey for maximum extent 2020 flood. Inter-annual were evaluated terms onset, recession frequency occurrence over analysis period (2017 2022) but with particular focus extreme events Borkena Dubti sites. Findings showed that significantly differed. RNID method, which allowed manual estimation threshold, provided highest capability both accuracy improved when normalizing signal backscatter intensity S-1 change method. onset noticeable difference across this indicate potential satellite remote sensing spatial temporal floods, further research needed improve these other affected

Язык: Английский

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