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Seabed hardness is an important character of seabed substrate as it may influence the nature of attachment of an organism to the seabed. Hence, spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is usually inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage or directly measured at limited locations. It can be predicted based on two-class hardness data derived from video footage and environmental predictors, but no study has been undertaken for predicting multiple-classes of hardness data. In this study, we classified the seabed hardness into four classes based on underwater video images that were extracted from the underwater video footage. We developed an optimal predictive model to predict the spatial distribution of seabed hardness using random forest (RF) based on the point data of the hardness classes and spatially continuous multibeam bathymetry, backscatter and other derived predictors. A novel model selection measure that is the averaged variable importance (AVI) was used based on predictive accuracy that was acquired from averaging the results of 100 times replication of 10-fold cross validation. Finally, the spatial predictions generated using the most accurate model were visually examined and analyzed in comparison with previously published predictions based on two-class hardness data. This study confirmed that: 1) seabed hardness of four classes can be predicted into a spatially continuous layer with a high degree of accuracy (i.e., with a correct classification rate of 86.27%); 2) model selection for RF is essential for identifying an optimal predictive model in environmental sciences and AVI selects the most accurate predictive model(s) instead of the most parsimonious ones, and is recommended for future studies; 3) caution should be taken when using the correlation coefficient to select predictors for RF in marine environmental sciences; 4) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in the environmental sciences; 5) the spatial predictions for four-class hardness data were similar with the predictions based on two hardness classes, with high match rates; and 6) RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies. In summary, this is the first attempt to predict the spatial distribution of seabed hardness of four classes. AVI shows its effectiveness in searching for the most accurate predictive models and is recommended for future studies. This study further confirms the superior performance of RF in marine environmental sciences. RF is an effective modelling method with high predictive accuracy not only for presence/absence data but also for multi-level categorical data. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies.

Predicting the spatial distribution of seabed hardness based on multiple categorical data using random forest