Using social media feeds for mapping and assessing areas affected by flooding due to tropical cyclones DOI Creative Commons
Patrick Ken Kalonde, Blessings Chiepa,

Alick Chisale Austin

и другие.

Journal of Flood Risk Management, Год журнала: 2023, Номер 16(4)

Опубликована: Июль 31, 2023

From February to March 2023, Tropical Cyclone Freddy caused widespread flooding and mudslides in Madagascar, Mozambique, most parts of Zimbabwe southern Malawi. In Malawi, it was reported that more than 511 people lost their lives, 533 remain missing, 563,602 displaced (reliefweb, 2023). According the Sendai Framework for Disaster Reduction, communities affected by disasters can build back better if past are used as a basis strengthening disaster risk reduction programs (United Nations, 2015). Therefore, this be practical real-world experiences well documented made available management programs. Ostensibly, with Freddy, we observed information about damages related response measures taken has been widely shared through social media platforms, especially WhatsApp, Twitter, Facebook. Thus, platforms present significant potential data sources operationalize provisions (especially priority action 4). It hazard later damage mostly screenshots indicating path progressed towards mainland Africa. At point, circulation originated from weather forecasting website services. After reached photographs videos showing damaged locations were shared, same happened analysis, posts not radiating central an individual or institution. Rather random individuals account vast quantities graphics associated Freddy. This only nourished public near-real-time but also captured areas contexts usually targeted mainstream (i.e., traditional media). Although is known lacking structure, equally seen one big create opportunities development disruptive innovations advancements data-driven science (Kitchin, 2014). So far, context flooding, previously flood water mapping (Fohringer et al., 2015; Rosser 2017), inundation modeling (Guan 2023; Ouyang 2022; Re 2022), providing valuable mitigation measures. However, previous tropical cyclones South-East Africa, rarely gathered organized direct efforts mitigate future similar events. Considering current situation, propose structured platform gather data. use keywords retrieve harvest hazards, how hazards interact human populations infrastructure developments s. stored on centralized where verified observer deliberate effort find location event happened. Malawi recently opened national center could purpose (Swinhoe, 2022). The integrated existing humanitarian tools such OpenStreetMap (Haklay & Weber, 2008), cutting-edge technologies Artificial Intelligence automate identification physical picture video captured. Equally, fused response, recovery, actions. We believe approach add value ongoing data-sharing practices promoting coordination transparency between relevant government authorities, organizations, public. must noted assess hindered limited presence active users, availability devices internet access considering 20.2% Malawi's population (Kemp, 2022) acceptability actors toward using feeds management. addition issues mentioned, developed might need have metrics indicate quality. One aspect likely suffer quality assigning graphic when written oral description available. can, however, mitigated employing multiple verifications score assigned verifier. conclusion, developing yet capturing generating meaningful may potentially bring transformation. cannot useful many countries common monitoring systems lacking. Data sharing applicable—no new generated.

Язык: Английский

Supercharging hydrodynamic inundation models for instant flood insight DOI
Niels Fraehr, Quan J. Wang, Wenyan Wu

и другие.

Nature Water, Год журнала: 2023, Номер 1(10), С. 835 - 843

Опубликована: Сен. 11, 2023

Язык: Английский

Процитировано

27

Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis DOI
Guangzhao Chen, Jingming Hou, Yuan Liu

и другие.

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

Опубликована: Март 8, 2024

Язык: Английский

Процитировано

16

Effects of urban drainage inlet layout on surface flood dynamics and discharge DOI
C. P. Liang, Mingfu Guan

Journal of Hydrology, Год журнала: 2024, Номер 632, С. 130890 - 130890

Опубликована: Фев. 15, 2024

Язык: Английский

Процитировано

12

A framework to assess suitability of global digital elevation models for hydrodynamic modelling in data scarce regions DOI

Vineela Nandam,

P. L. Patel

Journal of Hydrology, Год журнала: 2024, Номер 630, С. 130654 - 130654

Опубликована: Янв. 27, 2024

Язык: Английский

Процитировано

11

Risk assessment of flood disasters in the Poyang lake area DOI
Xianmin Wang, Wenxue Chen,

Jing Yin

и другие.

International Journal of Disaster Risk Reduction, Год журнала: 2023, Номер 100, С. 104208 - 104208

Опубликована: Дек. 20, 2023

Язык: Английский

Процитировано

20

Modeling Transient Mixed Flows in Drainage Networks With Smoothed Particle Hydrodynamics DOI
Wenke Song, Hexiang Yan,

Tao Tao

и другие.

Water Resources Management, Год журнала: 2024, Номер 38(3), С. 861 - 879

Опубликована: Янв. 10, 2024

Язык: Английский

Процитировано

7

A spatially distributed hydrodynamic model framework for urban flood hydrological and hydraulic processes involving drainage flow quantification DOI
Kaihua Guo, Mingfu Guan, Haochen Yan

и другие.

Journal of Hydrology, Год журнала: 2023, Номер 625, С. 130135 - 130135

Опубликована: Сен. 10, 2023

Язык: Английский

Процитировано

16

Dam break study and its flood risk in Gurara watershed-Nigeria under varied spatio-temporal conditions by integrating HSPF and HEC–RAS models DOI
Al−Amin Danladi Bello,

Abdullahi Sule Argungu,

Aminu Tijjani Soron Dinki

и другие.

Environmental Earth Sciences, Год журнала: 2024, Номер 83(4)

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

5

Interannual comparison of historical floods through flood detection using multi-temporal Sentinel-1 SAR images, Awash River Basin, Ethiopia DOI Creative Commons
Alemseged Tamiru Haile,

Tilaye Worku Bekele,

T.H.M. Rientjes

и другие.

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

Процитировано

11

Assessing LISFLOOD-FP with the next-generation digital elevation model FABDEM using household survey and remote sensing data in the Central Highlands of Vietnam DOI Creative Commons
Laurence Hawker, Jeffrey Neal, James Savage

и другие.

Natural hazards and earth system sciences, Год журнала: 2024, Номер 24(2), С. 539 - 566

Опубликована: Фев. 15, 2024

Abstract. Flooding is an endemic global challenge with annual damages totalling billions of dollars. Impacts are felt most acutely in low- and middle-income countries, where rapid demographic change driving increased exposure. These areas also tend to lack high-precision hazard mapping data which better understand or manage risk. To address this information gap a number flood models have been developed recent years. However, there substantial uncertainty over the performance these products. Arguably important component model digital elevation (DEM), must represent terrain without surface artifacts such as forests buildings. Here we develop evaluate next generation hydrodynamic based on recently released FABDEM DEM. We compare it previous version using MERIT DEM at three study sites Central Highlands Vietnam two independent validation sets household survey remotely sensed observations flooding. The consistently outperformed MERIT, agreement between remote sensing was greater than sets.

Язык: Английский

Процитировано

3