Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling...

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Bibliographic Details
Main Authors: Jin Li, Justy Siwabessy, Zhi Huang, Scott Nichol
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Geosciences
Subjects:
machine learning
variable importance
variable selection
model selection
predictive accuracy
spatial predictive model
acoustic multibeam data
spatial predictions
Online Access:https://www.mdpi.com/2076-3263/9/4/180

Internet

https://www.mdpi.com/2076-3263/9/4/180

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