| 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 | 2024, Apr. 26 |
| 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 LAC-CTE-INPE-MCTI-GOV-BR |
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) |
| Author e-Mail Address | 1 haroldo@lac.inpe.br |
| Editor | Khare, Mukesh |
| e-Mail Address | haroldo@lac.inpe.br |
| Book Title | Air Pokkution - Monitoring, Modeling and Health |
| Publisher | InTech |
| City | Rijeka |
| Pages | 259-280 |
| History (UTC) | 2012-06-22 00:11:01 :: lattes -> secretaria.cpa@dir.inpe.br :: 2012 2012-08-23 11:50:22 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012 2018-06-05 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 surface-atmosphere estimation Entropic regularized solution Artificial neural network Quasi-Newton 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 bio-geochemical 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 ground-atmosphere is an important issue for the bio-geochemical 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 ill-posed inverse problem becomes in a well-posed 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 L-curve technique. A recent approach for solving inverse problems is provided by the use of artificial neural networks (ANN). Neural networks are non-linear 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 (quasi-Newton), 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 meta-heuristic 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 inter-individual distances to synchronize the swarm behaviour. The swarm theory is then combined with a social-cognitive theory. The multilayer perceptron neural network (MLP-NN) 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 | InTech-Strategies_for_estimation.pdf |
| User Group | lattes secretaria.cpa@dir.inpe.br |
| Visibility | shown |
| 5. Allied materials | |
| Next Higher Units | 8JMKD3MGPCW/3ESGTTP |
| Citing Item List | sid.inpe.br/bibdigital/2013/09.22.23.14 1 |
| URL (untrusted data) | http://www.intechopen.com/books/air-pollution-monitoring-modelling-and-health/strategies-for-estimation-of-gas-mass-flux-rate-between-surface-and-the-atmosphere- |
| 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 schedulinginformation secondarydate secondarykey secondarymark serieseditor seriestitle session shorttitle sponsor subject tertiarymark tertiarytype translator versiontype volume |
| 7. Description control | |
| e-Mail (login) | marciana |
| update | |
