Skip to main content

Advertisement

Springer Nature Link
Log in

Combining social media data and meteorological sensors for urban flood detection: a statistical analysis in São Paulo City

  • Research
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Floods are among the most frequent and costly natural disasters in urban areas, often resulting from intense precipitation. Leveraging geospatial data from social media and physical sensors offers a valuable opportunity for effective flood detection. This study conducts a statistical analysis employing Anderson-Darling and Shapiro-Wilk tests to assess the normality of the data distributions. Correlation analyses were conducted to evaluate the relationships between rainfall levels, river levels, and Twitter (currently X), while the Mann-Whitney U test was used to compare data from flood and non-flood events. Meteorological variables, such as rainfall data from rain gauges and radar, proved critical in establishing a link between precipitation levels and flooding events. River level data from the São Paulo Flood Alert System revealed a strong correlation between river levels and flood conditions, particularly during “Warning” and “Emergency” situations. Additionally, the analysis of social media data demonstrated a significant correlation between the frequency of flood-related keywords in tweets and the occurrence of actual flood events. This finding highlights the potential of Twitter data as an alternative source for urban flood detection. By leveraging real-time, user-generated content, this approach offers a novel methodology for early warning systems, enhancing situational awareness and improving flood monitoring capabilities. The findings underscore the effectiveness of integrating multiple data sources for comprehensive flood monitoring, offering practical insights for improving flood detection and management in urban environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

No datasets were generated or analysed during the current study.

References

  • Albuquerque JP, Herfort B, Brenning A et al (2015) A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. Int J Geogr Inf Sci 29(4):667–689

    Article  Google Scholar 

  • Andrade SC, Porto de Albuquerque J, Restrepo-Estrada C et al (2022) The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events. Int J Geogr Inf Sci 36(6):1140–1165

    Article  Google Scholar 

  • Annis A, Nardi F (2019) Integrating vgi and 2d hydraulic models into a data assimilation framework for real time flood forecasting and mapping. Geo-spatial Inf Sci 22(4):223–236

    Article  Google Scholar 

  • Arshad M, Rasool M, Ahmad M (2003) Anderson darling and modified anderson darling tests for. Pakistan J Appl Sci 3(2):85–88

    Google Scholar 

  • Bertilsson L, Wiklund K, de Moura Tebaldi I et al (2019) Urban flood resilience-a multi-criteria index to integrate flood resilience into urban planning. J Hydrol 573:970–982

    Article  Google Scholar 

  • CGE (2022) São paulo’s emergency management center – flood records in são paulo, brazil, in 2019. https://www.cgesp.org/v3/

  • Douglas I, Garvin S, Lawson N et al (2010) Urban pluvial flooding: a qualitative case study of cause, effect and nonstructural mitigation. J Flood Risk Manag 3(2):112–125

    Article  Google Scholar 

  • Drews M, Steinhausen M, Larsen MAD et al (2023) The utility of using volunteered geographic information (vgi) for evaluating pluvial flood models. Sci Total Environ 894:164962. https://doi.org/10.1016/j.scitotenv.2023.164962, https://www.sciencedirect.com/science/article/pii/S0048969723035854

  • Gupta AK, Nair SS (2011) Urban floods in bangalore and chennai: risk management challenges and lessons for sustainable urban ecology. Current Science, pp 1638–1645

  • Haddad EA, Teixeira E (2015) Economic impacts of natural disasters in megacities: the case of floods in são paulo, brazil. Habitat Int 45:106–113

    Article  Google Scholar 

  • Hanusz Z, Tarasinska J, Zielinski W (2016) Shapiro-wilk test with known mean. REVSTAT-Stat J 14(1):89–100

    Google Scholar 

  • IBGE (2022) Fundação instituto brasileiro de geografia e estatística – panorama of brazilian municipalities. https://cidades.ibge.gov.br/brasil/sp/sao-paulo/panorama. Accessed 18 Feb 2022

  • Kocsis T, Kovács-Székely I, Anda A (2017) Comparison of parametric and non-parametric time-series analysis methods on a long-term meteorological data set. Cent Eur Geol 60(3):316–332

  • Kumar V, Sharma KV, Caloiero T et al (2023) Comprehensive overview of flood modeling approaches: a review of recent advances. Hydrology 10(7). https://doi.org/10.3390/hydrology10070141, https://www.mdpi.com/2306-5338/10/7/141

  • Laurien F, Keating A (2019) Evidence from measuring community flood resilience in asia. Asian Development Bank Economics Working Paper Series 12(595)

  • Manandhar B, Cui S, Wang L et al (2023) Urban flood hazard assessment and management practices in south asia: a review. Land 12(3):627

  • McKnight PE, Najab J (2010) Mann-whitney u test. The Corsini encyclopedia of psychology, pp 1–1

  • Morita M (2014) Flood risk impact factor for comparatively evaluating the main causes that contribute to flood risk in urban drainage areas. Water 6(2):253–270

  • Myers L, Sirois MJ (2014) Spearman correlation coefficients, differences between. Statistics Reference Online, Wiley StatsRef

    Book  Google Scholar 

  • Pereira Filho AJ, Santos CC (2006) Modeling a densely urbanized watershed with an artificial neural network, weather radar and telemetric data. J Hydrol 317(1–2):31–48

    Article  Google Scholar 

  • Poser K, Dransch D (2010) Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica 64(1):89–98

    Google Scholar 

  • Ray K, Pandey P, Pandey C et al (2019) On the recent floods in india. Curr Sci 117(2):204–218

    Article  Google Scholar 

  • Rodda JC (2011) Guide to hydrological practices. Hydrol Sci J 56(1):196–197. https://doi.org/10.1080/02626667.2011.546602

    Article  Google Scholar 

  • SAISP (2024) Fundação Centro Tecnológico de Hidráulica (FCTH). https://www.saisp.br/estaticos/sitenovo/produtos.xmlt#a7. Accessed 29 Aug 2024

  • Santos ET (2013) Impactos econômicos de desastres naturais em megacidades: o caso dos alagamentos em são paulo. Nome do Jornal 12(4):45–58

    Google Scholar 

  • Sharma VK, Rao GS, Amminedu E et al (2016) Event-driven flood management: design and computational modules. Geo-spatial Inf Sci 19(1):39–55. https://doi.org/10.1080/10095020.2016.1151212

    Article  Google Scholar 

  • Sharma VK, Mishra N, Shukla AK et al (2017) Satellite data planning for flood mapping activities based on high rainfall events generated using trmm, gefs and disaster news. Annals of GIS 23(2):131–140. https://doi.org/10.1080/19475683.2017.1304449

    Article  Google Scholar 

  • Shekhar H, Setty S (2015) Disaster analysis through tweets. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, pp 1719–1723

  • Silva CVF, Schardong A, Garcia JIB et al (2018) Climate change impacts and flood control measures for highly developed urban watersheds. Water 10(7):829

    Article  Google Scholar 

  • Siva L, Kimura R, Brambilla E et al (2023) Impacts of an urban flood control infrastructure on the limnology and ichthyofauna of a basaltic cuesta stream (southeast brazil). Braz J Biol 83:e276585

    Article  CAS  Google Scholar 

  • Tariq MAUR, Van De Giesen N (2012) Floods and flood management in pakistan. Phys Chem Earth, Parts A/B/C 47:11–20

    Article  Google Scholar 

  • Tomás LR, Soares GG, Jorge A et al (2022) Flood risk map from hydrological and mobility data: a case study in são paulo (brazil). arXiv e-prints pp arXiv–2204

  • Uijlenhoet R (2001) Raindrop size distributions and radar reflectivity-rain rate relationships for radar hydrology. Hydrol Earth Syst Sci 5(4):615–628

    Article  Google Scholar 

Download references

Funding

This research was funded by the São Paulo Research Foundation (FAPESP), grant 2021/01305-6, and National Council for Scientific and Technological Development (CNPq), grants 446053/2023-6 and 305220/2022-5.

Author information

Authors and Affiliations

  1. Institute of Science and Technology, São Paulo State University (UNESP), São José dos Campos, Brazil

    Vitor Yuichi Hossaki, Rogério Galante Negri, Tatiana Sussel Gonçalves Mendes & Adriano Bressane

  2. Brazilian Center for Early Warning and Monitoring for Natural Disasters (CEMADEN), Graduate Program in Natural Disasters, São Paulo State University (UNESP), São José dos Campos, Brazil

    Rogério Galante Negri & Tatiana Sussel Gonçalves Mendes

  3. Brazilian Center for Early Warning and Monitoring for Natural Disasters (CEMADEN), São José dos Campos, Brazil

    Leonardo Bacelar Lima Santos

  4. Graduate Program in Civil and Environmental Engineering, São Paulo State University (UNESP), Bauru, Brazil

    Adriano Bressane

Authors
  1. Vitor Yuichi Hossaki
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rogério Galante Negri
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Leonardo Bacelar Lima Santos
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Tatiana Sussel Gonçalves Mendes
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Adriano Bressane
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by V.Y.H, R.G.N and L.B.L.S. The first draft of the manuscript was written by V.Y.H, R.G.N and L.B.L.S. and all authors (V.Y.H, R.G.N, L.B.L.S., T.S.G.M and A.B.) commented on previous versions of the manuscript. V.Y.H, R.G.N, L.B.L.S., T.S.G.M and A.B. read and approved the final manuscript.

Corresponding author

Correspondence to Rogério Galante Negri.

Ethics declarations

Conflicts of Interest

The authors declare that they have no conflict of interest.

Competing interests

The authors declare no competing interests.

Additional information

Communicated by: Hassan Babaie.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hossaki, V.Y., Negri, R.G., Santos, L.B.L. et al. Combining social media data and meteorological sensors for urban flood detection: a statistical analysis in São Paulo City. Earth Sci Inform 18, 281 (2025). https://doi.org/10.1007/s12145-025-01802-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12145-025-01802-3

Keywords