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The search expression was <secondaryty cn and ref conference and firstg LAC-CTE-INPE-MCTI-GOV-BR and y 2012 and is *>.
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CR8AU8
Repositorysid.inpe.br/mtc-m19/2012/10.17.16.49
Last Update2012:10.17.16.52.26 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/10.17.16.49.15
Metadata Last Update2018:06.05.04.13.12 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN2238-1007
Citation KeyAraújoVelhGome:2012:MuOpEv
TitleMulti-objective Optimization by an Evolutionary Algorithm for Calibrating an Hydrological Model
Year2012
Access Date2023, Jan. 29
Secondary TypePRE CN
Number of Files1
Size854 KiB
2. Context
Author1 Araújo, Amarísio da S.
2 Velho, Haroldo F. de Campos
3 Gomes, Vitor C. F.
Group1
2 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 amarisio5@gmail.com
2 haroldo@lac.inpe.br
3 vitor.gomes@lac.inpe.br
Conference NameInternational Symposium on Uncertainty Quantification and Stochastic Modeling, 1.
Conference LocationSão Sebastião, SP
DateFeb. 26th to Mar. 2nd
Pages456-464
History (UTC)2012-10-17 16:52:26 :: marcelo.pazos -> administrator :: 2012
2018-06-05 04:13:12 :: administrator -> marcelo.pazos@inpe.br :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
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Content TypeExternal Contribution
Keywordsparameter estimation
hydrological model
calibration
multi-objective optimization
pareto set
AbstractHydrologic models simulate the river flow from the contributing basin for a given river. For the simulation process, the integration domain is discretized into computational cells. The inputs for such models are precipitation ratio and the initial flow. There are many parameters to be determined for an operational model, including the type of soil (porosity field, water flux between the bottom of the river and the water layer, among others). However, there is no unique set of parameters for representing the hydrology cycle. A multi-objective approach is employed to address the problem. The Pareto set is calculated for the IPH2 model by an epidemic genetic algorithm.
AreaCOMP
ArrangementMulti-objective Optimization by...
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6. Notes
Empty Fieldsarchivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn label language lineage mark nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid rightsholder secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url versiontype volume
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3C63DQP
Repositorydpi.inpe.br/plutao/2012/06.21.17.14
Last Update2012:09.03.19.08.46 (UTC) secretaria.cpa@dir.inpe.br
Metadata Repositorydpi.inpe.br/plutao/2012/06.21.17.14.13
Metadata Last Update2018:06.05.00.01.42 (UTC) administrator
ISBN22385851
Labellattes: 8216914793181349 1 BorgesGuim:2012:FoMuAj
Citation KeyBorgesGuim:2012:FoMuAj
TitleFonte Multiespectral Ajustável baseada em LEDs para Geração de Espectros Sintéticos necessários na calibração de Sensor Estelar
Year2012
Access Date2023, Jan. 29
Secondary TypePRE CN
Number of Files1
Size454 KiB
2. Context
Author1 Borges, Marcos Eduardo Gomes
2 Guimarães, L. N. F.
Group1 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 IEAV CTA
Author e-Mail Address1 marcoseborges@gmail.com
e-Mail Addressmarcoseborges@gmail.com
Conference NameSimpósio de Ciência e Tecnologia do Instituto de Estudos Avançados, 1.
Conference LocationSão José dos Campos
Date2012
Volume1
Pages42-47
Book TitleAnais
Tertiary TypeResumo Estendido
History (UTC)2012-06-22 00:10:59 :: lattes -> secretaria.cpa@dir.inpe.br :: 2012
2012-09-03 19:08:46 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2018-06-05 00:01:42 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsSensor de Estrelas
Fonte de luz
Controlador Fuzzy
Algoritmos Genéticos
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Fonte Multiespectral Ajustável...
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data URLhttp://urlib.net/ibi/J8LNKAN8RW/3C63DQP
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3C63DQP
Languagept
Target Filescti12_Borges_FonteMultiespectral_r01.pdf
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secretaria.cpa@dir.inpe.br
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Next Higher Units8JMKD3MGPCW/3ESGTTP
URL (untrusted data)http://scti.ieav.cta.br/
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsabstract archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition editor format issn lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid rightsholder secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark type versiontype
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CR8C35
Repositorysid.inpe.br/mtc-m19/2012/10.17.17.02
Last Update2012:10.17.17.38.20 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/10.17.17.02.43
Metadata Last Update2018:06.05.04.13.12 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN2238-1007
Citation KeyCintraVelh:2012:GlDaAs
TitleGlobal Data Assimilation Using Artificial Neural Networks in Speedy Model
Year2012
Access Date2023, Jan. 29
Secondary TypePRE CN
Number of Files1
Size875 KiB
2. Context
Author1 Cintra, Rosangela. S.
2 Velho, Haroldo F. de Campos
Group1
2 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 rosangela.cintra@lac.inpe.br
2 haroldo@lac.inpe.br
Conference NameInternational Symposium on Uncertainty Quantification and Stochastic Modeling, 1.
Conference LocationSão Sebastião, SP
DateFeb. 26th to Mar. 2nd, 2012
Pages648-654
Book TitleProceedings
History (UTC)2012-10-17 17:38:20 :: marcelo.pazos -> administrator :: 2012
2018-06-05 04:13:12 :: administrator -> marcelo.pazos@inpe.br :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsdata assimilation
artificial neural network
ensemble kalman Filter
numerical weather forecasting
AbstractWeather forecasting systems require a model for the time evolution and an estimate of the current state of the system. Data assimilation provides such an initial estimate of the atmosphere where it combines information from observations and from a prior short-term forecast producing an current state estimate. An Artificial Neural Network (ANN) is designed for data assimilation. The use of observations from the earth-orbiting satellites in operational numerical prediction models is performed for improving weather forecasts. The data related to atmospheric, oceanic, and land surface state from satellites provides increasingly large volumes. However, the use of this amount of data increases the computational effort. The goal here is to simulate the process for assimilating temperature data computed from satellite radiances. The numerical experiment is carried out with the global model Simplified parameterizations, primitive-Equation Dynamics (SPEEDY ) with simplified physical processes of an atmospheric general circulation in tri-dimensional coordinates. For the data assimilation scheme was applied an ANN: a Multilayer Perceptron(MLP) with supervised training. The MLP-ANN is able to emulate the analysis from the Local Ensemble Transform Kalman Filter(LETKF). LETKF is a version of Kalman Filter with Monte-Carlo ensembles of short-term forecasts. In this experiment, the MLP-ANN was trained with supervision from first six months considering the years 1982, 1983, and 1984. A hindcasting experiment for data assimilation performed a cycle for january of 1985 with MLP-NN, LETKF and SPEEDY model. The synthetic temperature observations were used. The numerical results demonstrate the effectiveness of this ANN technique on atmospheric data assimilation. The results for analysis with ANN are very close with the results from LETKF data assimilation. The simulations show that the major advantage of using MLP-NN is the better computational performance, with similar quality of analysis. The CPU-time assimilation with MLP-NN is 75% less than LETKF with the same observations. Actually, considering the supervised ANN for data assimilation, the most relevant issue is the computational speed-up for computing the analyzed initial condition for state model that accelerates the whole process of numerical weather prediction.
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6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn label language lineage mark nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid rightsholder secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url versiontype volume
7. Description control
e-Mail (login)marcelo.pazos@inpe.br
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CR8G42
Repositorysid.inpe.br/mtc-m19/2012/10.17.17.51
Last Update2012:10.17.17.53.37 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/10.17.17.51.56
Metadata Last Update2018:06.05.04.13.12 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN2238-1007
Citation KeyCortivoChalVelh:2012:CoTwLe
TitleComparison of two learning strategies for a supervised neural network
Year2012
Access Date2023, Jan. 29
Secondary TypePRE CN
Number of Files1
Size2149 KiB
2. Context
Author1 Cortivo, Fábio Dall
2 Chalhoub, Ezzat S.
3 Velho, Haroldo F. de Campos
Group1
2 LAC-CTE-INPE-MCTI-GOV-BR
3 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 fabio.cortivo@lac.inpe.br
2 ezzat@lac.inpe.br
3 haroldo@lac.inpe.br
e-Mail Addressmarcelo.pazos@inpe.br
Conference NameInternational Symposium on Uncertainty Quantification and Stochastic Modeling, 1.
Conference LocationSão Sebastião, SP
DateFeb. 26th to Mar. 2nd, 2012
Pages366-380
Book TitleProceedings
History (UTC)2012-12-27 16:35:11 :: marcelo.pazos@sid.inpe.br -> administrator :: 2012
2018-06-05 04:13:12 :: administrator -> marcelo.pazos@inpe.br :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsinverse problems
artificial neural networks
backpropagation
quasi-newton method
hydrologic optics
AbstractArtificial neural networks can be used to solve inverse problems. One relevant problem in hydrologic optics is the estimatation of the single scattering albedo from the emitted surface radiation. The multi-layer perceptron (MLP) can be applied to determine the albedo from the measured radiation. The MLP is designed with one hidden layer, where the activation employs the sigmoid function, with backpropagation for set-upping the network parameters. Using the generalized delta rule for the learning process to determine the weight connections, the neural inverse operator (ANN-1) produces good results with 20 inputs (10 incident beams, and 10 emitted beams) and 40 neurons in the hidden layer in two different groups of neurons (30 and 10), with two different parameters for the sigmoid function. The second scheme for training the neural estimator applies the quasi-Newton optimization. For the last strategy, the final neural inverse operator (ANN-2) has 10 inputs (emitted radiation) and 20 neurons in the hidden layer in two different groups of neurons (15 and 5). The measured data were emulated considering five levels of noise. For the generalization test, the ANN-1 and ANN-2 operators obtained 100% of correct answers for the noiseless observational data. For noisy data, the ANN-1 operator obtained 94% of correct answers, while the ANN-2 operator obtained 100% of correct answers. The main difference between these two ANNs is the training method, and the number of neurons in the input and the hidden layer.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Comparison of two...
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LinkingTrabalho Vinculado à Tese/Dissertação
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6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition editor format isbn label language lineage mark nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid rightsholder secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarytype type url volume
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CR8HAB
Repositorysid.inpe.br/mtc-m19/2012/10.17.18.06
Last Update2012:10.17.18.08.59 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/10.17.18.06.27
Metadata Last Update2018:06.05.04.13.13 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN2238-1007
Citation KeyFurtadoCampMaca:2012:DaAsNe
TitleData assimilation by neural network emulating representer method applied to the wave equation
Year2012
Access Date2023, Jan. 29
Secondary TypePRE CN
Number of Files1
Size750 KiB
2. Context
Author1 Furtado, Helaine C. M.
2 Campos Velho, Haroldo F. de
3 Macau, Elbert E. N.
Group1
2 LAC-CTE-INPE-MCTI-GOV-BR
3 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 helaine.furtado@lac.inpe.br
2 haroldo@lac.inpe.br
3 elbert@lac.inpe.br
e-Mail Addressmarcelo.pazos@inpe.br
Conference NameInternational Symposium on Uncertainty Quantification and Stochastic Modeling, 1.
Conference LocationSão Sebastião, SP
DateFeb. 26th to Mar. 2nd, 2012
Pages476-484
Book TitleProceedings
History (UTC)2012-10-17 18:08:59 :: marcelo.pazos@sid.inpe.br -> administrator :: 2012
2018-06-05 04:13:13 :: administrator -> marcelo.pazos@inpe.br :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsdata assimilation
neural network
variational method
representer method
wave equation
AbstractDescription of a physical phenomenon through differential equations has errors involved, since the mathematical model is always an approximation of reality. For an operational prediction system, one strategy to deal with uncertainties from the modeling and observation errors is to add some information from the real dynamics into mathematical model. This aditional information consists of observations on the phenomenon. However, the observational data insertion should be done carefully, for avoiding a worse performance of the prediction. Technical data assimilation are tools to combine data from physical-mathematics model with observational data to obtain a better forecast. Two data assimilation methods are compared here: the Kalman Filter method, and artificial neural network. Artificial neural networks appear as a novel method in the context for data assimilation. The performance of the methods is evaluated under application to wave propagation model (Bennet,2002).
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data URLhttp://urlib.net/ibi/8JMKD3MGP7W/3CR8HAB
zipped data URLhttp://urlib.net/zip/8JMKD3MGP7W/3CR8HAB
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6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition editor format isbn label language lineage mark nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid rightsholder secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url versiontype volume
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3C649N8
Repositorydpi.inpe.br/plutao/2012/06.21.21.52.18
Last Update2012:09.04.17.13.25 (UTC) secretaria.cpa@dir.inpe.br
Metadata Repositorydpi.inpe.br/plutao/2012/06.21.21.52.19
Metadata Last Update2018:06.05.00.01.51 (UTC) administrator
ISBN21774692
Labellattes: 3188890226171961 4 MoraisAsakBonfDant:2012:SiWeAl
Citation KeyMoraisAsakBonfDant:2012:SiWeAl
TitleSimulador Web de Algoritmos para Escalonamento de Processos em um Sistema Operacional
Year2012
Access Date2023, Jan. 29
Secondary TypePRE CN
Number of Files1
Size505 KiB
2. Context
Author1 Morais, Emerson L. de
2 Asakura, Henrique Y. O.
3 Bonfiglioli, Ricardo P.
4 Dantas, Murilo da Silva
Group1
2
3
4 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1 Faculdade de Tecnologia de São José dos Campos (FATEC-SJC)
2 Faculdade de Tecnologia de São José dos Campos (FATEC-SJC)
3 Faculdade de Tecnologia de São José dos Campos (FATEC-SJC)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 emerson.moraes@fatec.sp.gov.br
2 rique.hasakura@gmail.com
3 ricardo.bonfiglioli@gmail.com
4 murilodantas04@yahoo.com.br
e-Mail Addressmurilodantas04@yahoo.com.br
Conference NameEscola Regional de Computação Bahia Alagoas Sergipe, 12.
Conference LocationJuazeiro
Date2012
Book TitleAnais
Tertiary TypeArtigo
History (UTC)2012-06-22 00:11:01 :: lattes -> secretaria.cpa@dir.inpe.br :: 2012
2012-09-04 17:13:25 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2018-06-05 00:01:51 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsAlgoritmos
Escalonamento de Processos
Sistema Operacional
AbstractUm sistema operacional possui diversos conceitos complexos de software e o seu aprendizado pode ser comprometido. Em geral, isso ocorre devido à abstração necessária, à sua complexidade e ao seu número excessivo de funcionalidades, como gerenciamento de processos, sincronismo, tratamento de impasse e, em especial, no escalonamento de processos, foco deste trabalho. A simulação de algoritmos eleva a velocidade de aprendizado, pois demonstra na prática o funcionamento do sistema em si. Ao desenvolver sistemas simuladores dos conceitos abordados em sala de aula, tem-se um incremento substancial na absorção de conteúdos da computação por parte dos alunos. ABSTRACT: An operating system has several complex software concepts and their learning can be compromised. In general, this is due to abstraction required, its complexity and its excessive number of features such as process management, synchronization, deadlock handling, and in particular, process scheduling, focus of this work. The simulation algorithm increases the speed of learning, in practice it shows the operation of the system itself. By developing systems simulators of the concepts covered in class, it has been a substantial increase in the absorption of the contents of computing by students.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Simulador Web de...
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data URLhttp://urlib.net/ibi/J8LNKAN8RW/3C649N8
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3C649N8
Languagept
Target FileSimulador Web de Algoritmos para Escalonamento de Processos em um Sistema Operacional_Emerson-Henrique-Ricardo-Murilo.pdf
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Next Higher Units8JMKD3MGPCW/3ESGTTP
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6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition editor format issn lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid rightsholder secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3C63KEF
Repositorydpi.inpe.br/plutao/2012/06.21.18.22.16
Last Update2012:08.23.19.29.49 (UTC) secretaria.cpa@dir.inpe.br
Metadata Repositorydpi.inpe.br/plutao/2012/06.21.18.22.17
Metadata Last Update2021:01.03.02.12.49 (UTC) administrator
ISBN9788561203184
Labellattes: 9147853693310634 1 SantosAssiSilvAnge:2012:ReEpVa
Citation KeySantosAssiSilvAnge:2012:ReEpVa
TitleSobre risco, ameaça e vulnerabilidade à Leptospirose em situações pós-alagamentos, inundações e enxurradas: reconstruindo o episódio do Vale do Itajaí (2008-2009)
Year2012
Access Date2023, Jan. 29
Secondary TypePRE CN
Number of Files1
Size1000 KiB
2. Context
Author1 Santos, Leonardo Bacelar Lima
2 Assis, Mariane Carvalho
3 Silva, Ana Elisa Pereira
4 Angelis, Carlos Frederico
Group1 LAC-CTE-INPE-MCTI-GOV-BR
2 DPI-OBT-INPE-MCTI-GOV-BR
3 DSA-CPT-INPE-MCTI-GOV-BR
4 DSA-CPT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 santoslbl@gmail.com
e-Mail Addresssantoslbl@gmail.com
Conference NameSimpósio Brasileiro Sobre Desastres Naturais.
Conference LocationRio Claro
Date2012
Book TitleAnais
Tertiary TypeArtigo
History (UTC)2012-06-22 00:10:59 :: lattes -> secretaria.cpa@dir.inpe.br :: 2012
2012-08-23 19:29:50 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2021-01-03 02:12:49 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsdesastres naturais
leptospirose
hidrologia
Vale do Itajaí (SC)
natural disasters
leptospirosis
hydrology
Vale do Itajaí (SC)
AbstractO presente artigo traz um estudo de caso para a região do Vale do Itajaí no final de 2008, com o intuito de discutir a possibilidade de traçar cenários de risco de surtos de Leptospirose em situações pós desastres hidrológicos. Para tal esforço, têm-se como base a relação entre: i) a detecção da ameaça, ou seja, a presença do agente etiológico na região, e ii) a vulnerabilidade da área, agravada fortemente após a ocorrência de desastres naturais hidrológicos. No caso do Vale do Itajaí, tendo como base o monitoramento de precipitação, vazão e nível do rio Itajaí, e valendo-se da relação acima citada, as análises apresentadas neste artigo ilustram a possibilidade de traçar cenários de risco de surgimento de surtos de Leptospirose com antecedência de cerca de 3 semanas em relação ao pico da doença. ABSTRACT: This article presents a case study for the region of Vale do Itajai, 2008, in order to discuss the possibility of drawing up scenarios for risk of Leptospirosiss outbreaks after hydrological disasters. This effort has been based on the ratio between: i) the threat detection, the presence of the agent in the region, and ii) the vulnerability of the area, strongly worsened after the occurrence of hydrological disaster. In the case of Vale do Itajai, based on the monitoring of precipitation, flow and level of the Itajai river, the analyzes presented in this article illustrate the possibility of drawing up scenarios for risk until about three weeks before the peak of the disease.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/J8LNKAN8RW/3C63KEF
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3C63KEF
Languagept
Target FileLeonardo B. L. SANTOS (B).pdf
User Grouplattes
secretaria.cpa@dir.inpe.br
Visibilityshown
Read Permissionallow from all
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5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/43SRC6S
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
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