a50332d8-7ac9-42b6-bf2b-bbb5dc869a08
urn:uuid
eng
UTF-8
nonGeographicDataset
nonGeographicDataset
2026-01-20T06:09:09
creation
ISO 19115
2016
Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness
a50332d8-7ac9-42b6-bf2b-bbb5dc869a08
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.
<b>Citation:</b> Jin Li, Belinda Alvarez, Justy Siwabessy, Maggie Tran, Zhi Huang, Rachel Przeslawski, Lynda Radke, Floyd Howard, Scott Nichol, Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness, <i>Environmental Modelling & Software</i>, Volume 97, 2017, Pages 112-129, https://doi.org/10.1016/j.envsoft.2017.07.016
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112.92
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EARTH SCIENCES
Published_External
feature selection
machine learning - ML
model selection
predictive accuracy
spatial prediction
spatial predictive model
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