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Image processing for flood detection and prediction

Grant number: 20/05426-0
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Start date: August 01, 2020
End date: May 31, 2021
Field of knowledge:Engineering - Sanitary Engineering - Environmental Sanitation
Principal Investigator:Jó Ueyama
Grantee:Francisco Erivaldo Fernandes Junior
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

The 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. (AU)

News published in Agência FAPESP Newsletter about the scholarship:
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Scientific publications (4)
(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)
FERNANDES JR, FRANCISCO ERIVALDO; YEN, GARY G.. Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy. INFORMATION SCIENCES, v. 558, p. 91-102, . (20/05426-0)
FERNANDES JUNIOR, FRANCISCO ERIVALDO; NONATO, LUIS GUSTAVO; RANIERI, CAETANO MAZZONI; UEYAMA, JO. Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection. SENSORS, v. 21, n. 22, . (13/07375-0, 20/05426-0)
FERNANDES, JR., FRANCISCO E.; YEN, GARY G.. Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy. INFORMATION SCIENCES, v. 552, p. 29-47, . (20/05426-0)
FERNANDES JR, FRANCISCO E.; NONATO, LUIS GUSTAVO; UEYAMA, JO. A river flooding detection system based on deep learning and computer vision. MULTIMEDIA TOOLS AND APPLICATIONS, v. 81, n. 28, p. 21-pg., . (20/05426-0)