| 1. Identity statement | |
| Reference Type | Journal Article |
| Site | plutao.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 8JMKD3MGP3W/3MTN65S |
| Repository | sid.inpe.br/plutao/2016/12.05.19.53.48 |
| Last Update | 2016:12.07.11.27.10 (UTC) administrator |
| Metadata Repository | sid.inpe.br/plutao/2016/12.05.19.53.49 |
| Metadata Last Update | 2021:01.02.22.23.12 (UTC) administrator |
| DOI | 10.5923/s.ajee.201601.14 |
| ISSN | 2166-4633 2166-465X |
| Label | lattes: 2720072834057575 1 AnochiCamp:2016:MePrCl |
| Citation Key | AnochiCamp:2016:MePrCl |
| Title | Mesoscale precipitation climate prediction for brazilian south region by artificial neural networks  |
| Year | 2016 |
| Access Date | 2024, May 16 |
| Type of Work | journal article |
| Secondary Type | PRE PI |
| Number of Files | 1 |
| Size | 993 KiB |
| 2. Context | |
| Author | 1 Anochi, Juliana Aparecida 2 Campos Velho, Haroldo Fraga de |
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHC3 |
| Group | 1 DOP-CPT-INPE-MCTI-GOV-BR 2 LAC-CTE-INPE-MCTI-GOV-BR |
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) |
| Author e-Mail Address | 1 juliana.anochi@inpe.br 2 haroldo.camposvelho@inpe.br |
| Journal | American Journal of Environmental Engineering |
| Volume | 6 |
| Number | 4 |
| Pages | 94-102 |
| Secondary Mark | B3_GEOCIÊNCIAS B3_ENGENHARIAS_II B4_ENGENHARIAS_III |
| History (UTC) | 2016-12-05 19:53:49 :: lattes -> administrator :: 2016-12-07 03:44:30 :: administrator -> lattes :: 2016 2016-12-07 11:27:11 :: lattes -> administrator :: 2016 2021-01-02 22:23:12 :: administrator -> simone :: 2016 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Content Type | External Contribution |
| Version Type | publisher |
| Keywords | Climate prediction Precipitation Self-configured neural network Data reduction |
| Abstract | Numerical weather and climate use sophisticated mathematical models. These models are employed to simulate the atmospheric dynamics to perform a medium-range forecasting and climate prediction. Such an approach allows to estimate all meteorological variables for a future time period: wind fields, air temperature, pressure, moisture, and precipitation field. Precipitation is one of the most difficult fields for prediction. The latter statement is verified due to high variability in space and time. However, precipitation is a key issue in many activities of society. An alternative approach for climate prediction to the precipitation field is to employ the Artificial Neural Network (ANN). Such technique has a reduced computational cost in comparison with time integration of the partial differential equations. One challenge to employ an ANN is to determine the topology or configuration of a neural network. Here, a supervised ANN is designed to perform the precipitation prediction looking at two different periods: monthly and seasonal precipitation. The method is applied to the Southern region of Brazil. The definition of the neural network topology is addressed as an optimization problem. The best configuration is computed by minimizing a cost function. The optimization problem is solved by a new meta-heuristic: Multi-Particle Collision Algorithm (MPCA). In addition, a technique based on rough set theory is used to reduce the data space dimension. The predicted precipitation is evaluated by comparison with measured data. The prediction is also evaluated using full and reduced input data for a neural predictive model. |
| Area | COMP |
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Mesoscale precipitation climate... |
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDOP > Mesoscale precipitation climate... |
| 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/8JMKD3MGP3W/3MTN65S |
| zipped data URL | http://urlib.net/zip/8JMKD3MGP3W/3MTN65S |
| Language | en |
| User Group | lattes self-uploading-INPE-MCTI-GOV-BR |
| Reader Group | administrator lattes |
| Visibility | shown |
| Archiving Policy | allowpublisher allowfinaldraft |
| Read Permission | allow from all |
| Update Permission | not transferred |
| 5. Allied materials | |
| Linking | 8JMKD3MGP3W34P/3K98PDP |
| Mirror Repository | urlib.net/www/2011/03.29.20.55 |
| Next Higher Units | 8JMKD3MGPCW/3ESGTTP 8JMKD3MGPCW/43SQKNE |
| Citing Item List | sid.inpe.br/bibdigital/2013/09.22.23.14 4 sid.inpe.br/mtc-m21/2012/07.13.14.49.40 3 |
| URL (untrusted data) | http://article.sapub.org/10.5923.s.ajee.201601.14.html |
| Host Collection | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notes | |
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress format isbn lineage mark month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject targetfile tertiarytype |
| 7. Description control | |
| e-Mail (login) | simone |
| update | |