| bibtype |
J -
Journal Article
|
| ARLID |
0457037 |
| utime |
20240103211949.0 |
| mtime |
20160316235959.9 |
| SCOPUS |
84959423146 |
| WOS |
000374602000018 |
| DOI |
10.1016/j.envsoft.2016.02.002 |
| title
(primary) (eng) |
Sparse optimization for inverse problems in atmospheric modelling |
| specification |
| page_count |
11 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0252838 |
| ISSN |
1364-8152 |
| title
|
Environmental Modelling & Software |
| volume_id |
79 |
| volume |
3 (2016) |
| page_num |
256-266 |
| publisher |
|
|
| keyword |
Inverse modelling |
| keyword |
Sparse optimization |
| keyword |
Integer optimization |
| keyword |
Least squares |
| keyword |
European tracer experiment |
| keyword |
Free Matlab codes |
| author
(primary) |
| ARLID |
cav_un_auth*0309054 |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| full_dept |
Department of Decision Making Theory |
| name1 |
Adam |
| name2 |
Lukáš |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0280972 |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Decision Making Theory |
| name1 |
Branda |
| name2 |
Martin |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0318110 |
| project_id |
7F14287 |
| agency |
GA MŠk |
| country |
CZ |
|
| abstract
(eng) |
We consider inverse problems in atmospheric modelling represented by a linear system which is based on a source-receptor sensitivity matrix and measurements. Instead of using the ordinary least squares, we add a weighting matrix based on the topology of measurement points and show the connection with Bayesian modelling. Since the source-receptor sensitivity matrix is usually ill-conditioned, the problem is often regularized, either by perturbing the objective function or by modifying the sensitivity matrix. However, both these approaches may be heavily dependent on specified parameters. To ease this burden, we propose to use techniques looking for a sparse solution with a small number of positive elements. Finally, we compare all these methods on the European Tracer Experiment (ETEX) data where there is no apriori information apart from the release position and some measurements. |
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0258406 |
| confidential |
S |
| mrcbC86 |
3+4 Article Computer Science Interdisciplinary Applications|Engineering Environmental|Environmental Sciences|Water Resources |
| mrcbT16-e |
COMPUTERSCIENCEINTERDISCIPLINARYAPPLICATIONS|ENGINEERINGENVIRONMENTAL|ENVIRONMENTALSCIENCES |
| mrcbT16-j |
1.21 |
| mrcbT16-s |
1.986 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
81.946 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1 |
| arlyear |
2016 |
| mrcbU14 |
84959423146 SCOPUS |
| mrcbU34 |
000374602000018 WOS |
| mrcbU63 |
cav_un_epca*0252838 Environmental Modelling & Software 1364-8152 1873-6726 Roč. 79 č. 3 2016 256 266 Elsevier |
|