Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

Full text
Author(s):
Furquim, Gustavo [1] ; Pessin, Gustavo [2] ; Faical, Bruno S. [1] ; Mendiondo, Eduardo M. [3] ; Ueyama, Jo [1]
Total Authors: 5
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: NEURAL COMPUTING & APPLICATIONS; v. 27, n. 5, SI, p. 1129-1141, JUL 2016.
Web of Science Citations: 7
Abstract

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)

FAPESP's process: 08/58161-1 - Assessment of impacts and vulnerability to climate change in Brazil and strategies for adaptation option
Grantee:Jose Antonio Marengo Orsini
Support type: Research Program on Global Climate Change - Thematic Grants
FAPESP's process: 14/19076-0 - Providing Smarer Wireless Sensor Networks and UAVs: A Study in the Autonomous Networks Laboratory at USC
Grantee:Jó Ueyama
Support type: Scholarships abroad - Research
FAPESP's process: 12/22550-0 - Exploiting the sensor web and participatory sensing approaches for urban river monitoring
Grantee:Jó Ueyama
Support type: Regular Research Grants