Physical- and Social-Based Rain Gauges—A Case Study on Urban Flood Detection

Vitor Y. Hossaki; Wilson F. M. S. Seron; Rogério G. Negri; Luciana R. Londe; Lívia R. Tomás; Roberta B. Bacelar; Sidgley C. Andrade; Leonardo B. L. Santos

doi:10.3390/geosciences13040111

Geosciences (Apr 2023)

Physical- and Social-Based Rain Gauges—A Case Study on Urban Flood Detection

Vitor Y. Hossaki,
Wilson F. M. S. Seron,
Rogério G. Negri,
Luciana R. Londe,
Lívia R. Tomás,
Roberta B. Bacelar,
Sidgley C. Andrade,
Leonardo B. L. Santos

Affiliations

Vitor Y. Hossaki: Sciences and Technology Institute, São José dos Campos, State University (UNESP), São José dos Campos 12224-300, Brazil
Wilson F. M. S. Seron: Digital Media and Society Observatory, Federal University of São Paulo (UNIFESP), São José dos Campos 12231-280, Brazil
Rogério G. Negri: Sciences and Technology Institute, São José dos Campos, State University (UNESP), São José dos Campos 12224-300, Brazil
Luciana R. Londe: National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), São José dos Campos 12247-016, Brazil
Lívia R. Tomás: National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), São José dos Campos 12247-016, Brazil
Roberta B. Bacelar: Department of Communication, Anhanguera College, São José dos Campos 12236-660, Brazil
Sidgley C. Andrade: Federal University of Technology—Paraná (UTFPR), Toledo 85902-490, Brazil
Leonardo B. L. Santos: National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), São José dos Campos 12247-016, Brazil

DOI: https://doi.org/10.3390/geosciences13040111
Journal volume & issue: Vol. 13, no. 4
p. 111

Abstract

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Floods are among the most frequent and costly rainfall-triggered disasters. In this context, geospatial content generated by non-professionals using geolocated systems offers the possibility of monitoring environmental events. This study shows a statistical correlation between in situsensors, radar, Twitter posts, and flooding events. Furthermore, we observed in this study that flooding-related keywords are statistically more significant on flooding days than on non-flooding days and reinforce that Twitter can be employed as a complementary data source for flood management systems.

Published in Geosciences

ISSN: 2076-3263 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science: Geology
Website: https://www.mdpi.com/journal/geosciences

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Abstract

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