Potential distribution modelling using machine learning

被引：0
|
作者：
Lorena, Ana C. [1 ]
de Siqueira, Marinez F. [2 ]
De Giovanni, Renato
de Carvalho, Andre C. P. L. F. [3 ]
Prati, Ronaldo C. [3 ]
机构：
[1] Univ Fed ABC, Ctr Matemat Computacao & Cognicao, Santo Andre, SP, Brazil
[2] Ctr Referencia Inform Ambiental, Campinas, SP, Brazil
[3] Univ Sao Paulo, Inst Ciencia Math Comp, Sao Carlos, SP, Brazil
来源：
NEW FRONTIERS IN APPLIED ARTIFICIAL INTELLIGENCE | 2008年 / 5027卷
基金：
巴西圣保罗研究基金会;
关键词：
ecological niche modelling; potential distribution modelling; machine learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 obovatum Benth (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.
引用
收藏
页码：255 / +
页数：3
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