Image processing for flood detection and prediction

• Grant number: 21/10921-2
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: December 01, 2021
• End date: June 30, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Jó Ueyama
• Grantee: Caetano Mazzoni Ranieri
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Floods have been problematic in Brazil often causing material and life losses. Several cities like São Paulo, São Carlos, Osasco and Mogi das Cruzes have suffered from the floods. The group responsible for this research carried out relevant work in the area of flood detection using technologies such as the internet of things. Flood detection sensors were installed in the city of São Carlos in partnership with the town hall of São Carlos-SP, Brazil. This project aims to detect and predict flooding using images and thus automate the process of flood identification without human intervention. Such an approach only uses cameras without the need for the river height sensor that remains submerged in the urban streams. River height sensors are usually susceptible to failure as they are continually in contact with river water. In addition, Civil Defense bodies often require flooded river images and therefore we believe that the use of image processing for flood detection is timely as one single sensor (i.e. a camera) is needed to detect floods and provide images to Civil Defense bodies. Different models for flood prediction based on river-side cameras will be designed, and contextualized analyses considering different types of modalities will be performed. (AU)

Scientific publications

(8)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

RANIERI, CAETANO M.; FOLETTO, ANGELO V. K.; GARCIA, RODRIGO D.; MATOS, SAULO N.; MEDINA, MARIA M. G.; MARCOLINO, LEANDRO S.; UEYAMA, JO. Water level identification with laser sensors, inertial units, and machine learning. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v. 127, p. 17-pg., 2023-10-11. (22/09644-7, 21/10921-2, 13/07375-0)

MOSAIYEBZADEH, FATEMEH; POURIYEH, SEYEDAMIN; PARIZI, REZA M.; SHENG, QUAN Z.; HAN, MENG; ZHAO, LIANG; SANNINO, GIOVANNA; RANIERI, CAETANO MAZZONI; UEYAMA, JO; BATISTA, DANIEL MACEDO. Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey. ELECTRONICS, v. 12, n. 12, p. 28-pg., 2023-06-01. (21/10921-2, 14/50937-1, 15/24485-9, 13/07375-0)

BRITO, LUCAS A. V.; MENEGUETTE, RODOLFO I.; DE GRANDE, ROBSON E.; RANIERI, CAETANO M.; UEYAMA, JO. FLORAS: urban flash-flood prediction using a multivariate model. APPLIED INTELLIGENCE, v. N/A, p. 19-pg., 2022-12-02. (20/07162-0, 21/10921-2)

CARDOSO E SILVA, ALEF VINICIUS; GIUNTINI, FELIPE TALIAR; RANIERI, CAETANO MAZZONI; MENEGUETTE, RODOLFO IPOLITO; GARCIA, RODRIGO DUTRA; RAMACHANDRAN, GOWRI SANKAR; KRISHNAMACHARI, BHASKAR; UEYAMA, JO. MADCS: A Middleware for Anomaly Detection and Content Sharing for Blockchain-Based Systems. Journal of Network and Systems Management, v. 31, n. 3, p. 29-pg., 2023-07-01. (21/10921-2, 13/07375-0, 18/17335-9)

MATOS, SAULO NEVES; ROCHA, ARTHUR LIMA MARQUES; DOMINGUES FILHO, GABRIEL MONTAGNI; RANIERI, CAETANO MAZZONI; GARCIA, RODRIGO DUTRA; FARIA, ANA CLARA DE OLIVEIRA; MEDINA, MARIA MERCEDES GAMBOA; UEYAMA, JO. Ensuring reliable water level measurement for flooding: A redundancy-based approach with pressure transducer and computer vision. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, v. N/A, p. 12-pg., 2024-12-31. (21/10921-2, 22/09644-7, 13/07375-0)

RANIERI, CAETANO M.; MOIOLI, RENAN C.; VARGAS, PATRICIA A.; ROMERO, ROSELI A. F.. A neurorobotics approach to behaviour selection based on human activity recognition. COGNITIVE NEURODYNAMIC, v. N/A, p. 20-pg., 2022-09-27. (18/25902-0, 21/10921-2, 13/07375-0, 17/02377-5, 17/01687-0)

RANIERI, CAETANO MAZZONI; SOUZA, THAIS LUIZA DONEGA E; NISHIJIMA, MARISLEI; KRISHNAMACHARI, BHASKAR; UEYAMA, JO. A deep learning workflow enhanced with optical flow fields for flood risk estimation. APPLIED INTELLIGENCE, v. 54, n. 7, p. 22-pg., 2024-04-22. (22/09644-7, 21/10921-2, 13/07375-0)

VALENTINI, EDIVALDO PASTORI; ROCHA FILHO, GERALDO PEREIRA; DE GRANDE, ROBSON EDUARDO; RANIERI, CAETANO MAZZONI; PEREIRA JUNIOR, LOURENCO ALVES; MENEGUETTE, RODOLFO IPOLITO. A Novel Mechanism for Misbehavior Detection in Vehicular Networks. IEEE ACCESS, v. 11, p. 14-pg., 2023-01-01. (21/06210-3, 13/07375-0, 20/07162-0, 21/10921-2)