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

et al.

Journal of Flood Risk Management, Journal Year: 2023, Volume and Issue: 16(4)

Published: July 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.

Language: Английский

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

et al.

Nature Water, Journal Year: 2023, Volume and Issue: 1(10), P. 835 - 843

Published: Sept. 11, 2023

Language: Английский

Citations

27

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

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 131059 - 131059

Published: March 8, 2024

Language: Английский

Citations

16

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, Journal Year: 2024, Volume and Issue: 630, P. 130654 - 130654

Published: Jan. 27, 2024

Language: Английский

Citations

11

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

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130890 - 130890

Published: Feb. 15, 2024

Language: Английский

Citations

11

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

Jing Yin

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2023, Volume and Issue: 100, P. 104208 - 104208

Published: Dec. 20, 2023

Language: Английский

Citations

20

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

Tao Tao

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(3), P. 861 - 879

Published: Jan. 10, 2024

Language: Английский

Citations

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

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130135 - 130135

Published: Sept. 10, 2023

Language: Английский

Citations

15

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

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(4)

Published: Feb. 1, 2024

Language: Английский

Citations

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

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 124, P. 103505 - 103505

Published: Sept. 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

Language: Английский

Citations

11

Solving the Discretised Shallow Water Equations Using Non-Uniform Grids and Machine-Learning Libraries DOI
Amin Nadimy, Boyang Chen, Zhe Chen

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0