Name
Abstract | ||
|---|---|---|
Potential distribution modelling has been widely used to predict and to understand the geographical distribution of species. These models are generally produced by retrieving the environmental conditions where the species is known to be present or absent and feeding this data into a modelling algorithm. This paper investigates the use of Machine Learning techniques in the potential distribution modelling of plant species Stryphnodendron obovatumBenth (MIMOSACEAE). Three techniques were used: Support Vector Machines, Genetic Algorithms and Decision Trees. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species being considered. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69052-8_27 | IEA/AIE |
Keywords | Field | DocType |
genetic algorithms,plant species stryphnodendron obovatumbenth,modelling algorithm,decision trees,potential distribution modelling,potential distribution,geographical distribution,distribution profile,environmental condition,machine learning technique,support vector machine,machine learning,decision tree,genetic algorithm | Decision tree,Data mining,Environmental niche modelling,Computer science,Support vector machine,Artificial intelligence,Stryphnodendron,Genetic algorithm,Machine learning,Plant species | Conference |
Volume | ISSN | Citations |
5027 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana C. Lorena | 1 | 94 | 7.25 |
| Marinez F. Siqueira | 2 | 11 | 1.21 |
| Renato Giovanni | 3 | 48 | 5.35 |
| André C. Carvalho | 4 | 134 | 4.31 |
| Ronaldo C. Prati | 5 | 1043 | 48.03 |