ef01e60c-c6f5-4c1d-a7e8-f342752df8ee urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-10-17T01:25:31 creation ISO 19115 2016 Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods ef01e60c-c6f5-4c1d-a7e8-f342752df8ee 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 (GBM), to seabed sand content point data and acoustic multibeam data and their derived variables, to develop an accurate model to predict seabed sand content at a local scale. We also addressed relevant issues with variable selection. It was found that: (1) backscatter-related variables are more important than bathymetry-related variables for sand predictive modelling; (2) the inclusion of highly correlated predictors can improve predictive accuracy; (3) the rank orders of averaged variable importance (AVI) and accuracy contribution change with input predictors for RF and are not necessarily matched; (4) a knowledge-informed AVI method (KIAVI2) is recommended for RF; (5) the hybrid methods and their averaging can significantly improve predictive accuracy and are recommended; (6) relationships between sand and predictors are non-linear; and (7) variable selection methods for GBM need further study. Accuracy-improved predictions of sand content are generated at high resolution, which provide important baseline information for environmental management and conservation. Citation: Li, J.; Siwabessy, J.; Huang, Z.; Nichol, S. Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods. Geosciences 2019, 9, 180. https://doi.org/10.3390/geosciences9040180 {"type": "Polygon", "coordinates": [[[112.0, -44.0], [154.0, -44.0], [154.0, -9.0], [112.0, -9.0], [112.0, -44.0]]]} 112.0 154.0 -44.0 -9.0 EARTH SCIENCES Published_External acoustic multibeam data machine learning model selection predictive accuracy spatial predictions spatial predictive model variable importance variable selection Link to Journal documentDigital Link to Journal HTML