Name
Abstract | ||
|---|---|---|
Kriging is one of the most used spatial estimation methods in real-world applications. In kriging estimation, some parameters must be estimated in order to reach a good accuracy in the interpolation process, however, this step is still a challenge. Various optimization methods have been tested to find good parameters to this process, however, in recent years, many authors are using bioinspired techniques and reaching good results in estimating these parameters. This paper presents a comparison between well-known bio-inspired techniques such as Genetic Algorithms, Differential Evolution and Particle Swarm Optimization in the estimation of the essential kriging parameters: nugget, sill, range, angle, and factor. We also proposed an improved cluster-based kriging method to perform the tests. The results shows that the algorithms have a similar accuracy in estimating these parameters, and the number of clusters have a high impact on the results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-24289-3_54 | COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT I: 19TH INTERNATIONAL CONFERENCE, SAINT PETERSBURG, RUSSIA, JULY 1-4, 2019, PROCEEDINGS, PT I |
Keywords | Field | DocType |
Bio-inspired algorithms, Artificial Intelligence, Geostatistic, Kriging | Kriging,Particle swarm optimization,Cluster (physics),Computer science,Interpolation,Algorithm,Differential evolution,Kriging method,Genetic algorithm | Conference |
Volume | ISSN | Citations |
11619 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Yasojima | 1 | 0 | 0.68 |
| Tamara Ramos | 2 | 0 | 0.34 |
| Tiago Araújo | 3 | 0 | 0.34 |
| Bianchi Serique Meiguins | 4 | 56 | 28.03 |
| Nelson Neto | 5 | 0 | 0.34 |
| Jefferson Morais | 6 | 4 | 4.18 |