Application of random forest and generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Research output: Contribution to journalArticle

Abstract

Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management, but they are either unavailable or unknown. Hence we applied random forest (RF), generalised linear model (GLM) and their hybrid methods with geostatistical techniques to SSR data by addressing relevant issues with variable selection and model selection. It was found that: 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model selection methods are unsuitable for identifying GLM predictive models and joint application of RF and AIC can select accuracy-improved models; 3) highly correlated predictors may improve RF predictive accuracy; 4) hybrid methods for RF can accurately predict count data; and 5) effects of model averaging are method-dependent. This study depicted the non-linear relationships of SSR and predictors, generated spatial distribution of SSR with high accuracy and revealed the association of high SSR with hard seabed features.

Details

Authors
  • Jin-Song Li
  • Belinda Alvarez
  • Justy Siwabessy
  • Maggie Tran
  • Zhi Huang
  • Rachel Przeslawski
  • Lynda Radke
  • Floyd Howard
  • Scott Nichol
External organisations
  • Museum and Art Gallery of the Northern Territory
  • Geoscience Australia
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Earth and Related Environmental Sciences

Keywords

  • Feature selection, Machine learning, Model selection, Predictive accuracy, Spatial prediction, Spatial predictive model
Original languageEnglish
Pages (from-to)112-129
Number of pages18
JournalEnvironmental Modelling and Software
Volume97
Publication statusPublished - 2017 Nov 1
Publication categoryResearch
Peer-reviewedYes
Externally publishedYes