1. Identity statement  
Reference Type  Book Section 
Site  plutao.sid.inpe.br 
Holder Code  isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S 
Identifier  J8LNKAN8RW/3C643U2 
Repository  dpi.inpe.br/plutao/2012/06.21.20.42 
Last Update  2012:08.23.11.40.06 (UTC) secretaria.cpa@dir.inpe.br 
Metadata Repository  dpi.inpe.br/plutao/2012/06.21.20.42.49 
Metadata Last Update  2018:06.05.00.01.49 (UTC) administrator 
ISBN  9789535104 
Label  lattes: 5142426481528206 1 CamposVelhoRobeLuzPaes:2012:StEsGa 
Citation Key  CamposVelhoRobeLuzPaes:2012:StEsGa 
Title  Strategies for Estimation of Gas Mass Flux Rate Between Surface and the Atmosphere 
Year  2012 
Access Date  2023, Jan. 29 
Secondary Type  PRE LI 
Number of Files  1 
Size  811 KiB 

2. Context  
Author  1 Campos Velho, Haroldo Fraga de 2 Roberti, Débora R 3 Luz, Eduardo Fávero Pacheco da 4 Paes, Fabiana Ferreira 
Group  1 LACCTEINPEMCTIGOVBR 
Affiliation  1 Instituto Nacional de Pesquisas Espaciais (INPE) 
Author eMail Address  1 haroldo@lac.inpe.br 
Editor  Khare, Mukesh 
eMail Address  haroldo@lac.inpe.br 
Book Title  Air Pokkution  Monitoring, Modeling and Health 
Publisher  InTech 
City  Rijeka 
Pages  259280 
History (UTC)  20120622 00:11:01 :: lattes > secretaria.cpa@dir.inpe.br :: 2012 20120823 11:50:22 :: secretaria.cpa@dir.inpe.br > administrator :: 2012 20180605 00:01:49 :: administrator > marciana :: 2012 

3. Content and structure  
Is the master or a copy?  is the master 
Content Stage  completed 
Transferable  1 
Content Type  External Contribution 
Keywords  Gas flux surfaceatmosphere estimation Entropic regularized solution Artificial neural network QuasiNewton optimization Particle Swarm Optimization Inverse air pollution problem 
Abstract  A relevant issue nowadays is the monitoring and identification of the concentration and rate flux of the gases from the greenhouse effect. Most of these minority gases belong to important biogeochemical cycles between the planet surface and the atmosphere. Therefore, there is an intense research agenda on this topic. Here, we are going to describe the effort for addressing this challenging. This identification problem can be formulated as an inverse problem. The problem for identifying the minority gas emission rate for the system groundatmosphere is an important issue for the biogeochemical cycle, and it has being intensively investigated. This inverse problem has been solved using regularized solutions (Kasibhatla, 2000), Bayes estimation (Enting, 2002; Gimson & Uliasz, 2003), and variational methods (Elbern et al., 2007) the latter approach coming from the data assimilation studies. The inverse solution could be computed by calculating the regularized solutions. Regularization is a general mathematical procedure for dealing with inverse problems, looking for the smoothest (regular) inverse solution. For this approach, the inverse problem can be formulated as an optimization problem with constrains (a priori information). These constrains can be added to the objective function with the help of a regularization parameter. For adding a regularization operator, the illposed inverse problem becomes in a wellposed one. The maximum second order entropy principle was used as a regularization operator (Ramos et al., 1999), and the regularization parameter is found by the Lcurve technique. A recent approach for solving inverse problems is provided by the use of artificial neural networks (ANN). Neural networks are nonlinear mappings, and it is possible to design an ANN to be one inverse operator, with robustness to deal on noisy data. We have applied two different strategies for addressing the inverse problem: formulated as an optimization problem, and neural network. The optimization problem has been solved by using a deterministic method (quasiNewton), implementation used in the E04UCF routine (NAG, 1995) see Roberti et al. (2007), and applying a stochastic scheme (Particle Swarm Optimization: PSO) (Luz et al., 2007). The PSO is a metaheuristic based on the collaborative behaviour of biological populations. The algorithm is based on the swarm theory, proposed by Kennedy & Eberhart (1995) and is inspired in the flying pattern of birds which can be achieve by manipulating interindividual distances to synchronize the swarm behaviour. The swarm theory is then combined with a socialcognitive theory. The multilayer perceptron neural network (MLPNN) with 2 hidden layers (with 15 and 30 neurons) was applied to estimate the rate of surface emission of a greenhouse gas (Paes et al., 2010). The input for the ANN is the gas concentration measured on a set of points. 
Area  COMP 
Arrangement  urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Strategies for Estimation... 
doc Directory Content  access 
source Directory Content  there are no files 
agreement Directory Content  there are no files 

4. Conditions of access and use  
data URL  http://urlib.net/ibi/J8LNKAN8RW/3C643U2 
zipped data URL  http://urlib.net/zip/J8LNKAN8RW/3C643U2 
Language  en 
Target File  InTechStrategies_for_estimation.pdf 
User Group  lattes secretaria.cpa@dir.inpe.br 
Visibility  shown 

5. Allied materials  
Next Higher Units  8JMKD3MGPCW/3ESGTTP 
URL (untrusted data)  http://www.intechopen.com/books/airpollutionmonitoringmodellingandhealth/strategiesforestimationofgasmassfluxratebetweensurfaceandtheatmosphere 
Host Collection  dpi.inpe.br/plutao@80/2008/08.19.15.01 

6. Notes  
Empty Fields  archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition format issn lineage mark mirrorrepository nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark serieseditor seriestitle session shorttitle sponsor subject tertiarymark tertiarytype translator versiontype volume 

7. Description control  
eMail (login)  marciana 
update  
