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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory

Texto completo
Furquim, Gustavo [1] ; Pessin, Gustavo [2] ; Faical, Bruno S. [1] ; Mendiondo, Eduardo M. [3] ; Ueyama, Jo [1]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, Sao Carlos, SP - Brazil
[2] Vale Inst Technol, Appl Comp Lab, Belem, Para - Brazil
[3] Univ Sao Paulo, Sao Carlos Sch Engn EESC, Sao Carlos, SP - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: NEURAL COMPUTING & APPLICATIONS; v. 27, n. 5, SI, p. 1129-1141, JUL 2016.
Citações Web of Science: 7

Monitoring natural environments is a challenging task on account of their hostile features. The use of wireless sensor networks (WSNs) for data collection is a feasible method since these domains lack any infrastructure. However, further studies are required to handle the data collected for a better modeling of behavior and thus make it possible to forecast impending disasters. In light of this, in this paper an analysis is conducted on the use of data gathered from urban rivers to forecast flooding with a view to reducing the damage it causes. The data were collected by means of a WSN in Sao Carlos, Sao Paulo State, Brazil, which gathered and processed data about the river level and rainfall by means of machine learning techniques and employing chaos theory to model the time series; this meant that the inputs of the machine learning technique were the time series gathered by the WSN modeled on the basis of the immersion theorem. The WSNs were deployed by our group in the city of Sao Carlos where there have been serious problems caused by floods. After the data interdependence had been established by the immersion theorem, the artificial neural networks were investigated to determine their degree of accuracy in the forecasting models. (AU)

Processo FAPESP: 14/19076-0 - Provendo Redes de Sensores sem Fio e VANTs mais Inteligentes: Estágio no Grupo de Redes Autônomas da USC
Beneficiário:Jó Ueyama
Linha de fomento: Bolsas no Exterior - Pesquisa
Processo FAPESP: 12/22550-0 - Explorando a abordagem sensor web e o sensoriamento participatório no monitoramento de rios urbanos
Beneficiário:Jó Ueyama
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 08/58161-1 - Assessment of impacts and vulnerability to climate change in Brazil and strategies for adaptation option
Beneficiário:Jose Antonio Marengo Orsini
Linha de fomento: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - Temático