Home > Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia
Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia
| Title | Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia |
| Publication Type | Journal Article |
| Year of Publication | 2012 |
| Authors | Huang, Z, Nichol SL, Siwabessy JPW, Daniell J, Brooke BP |
| Journal | International Journal of Geographical Information Science |
| Volume | 26 |
| Issue | 2 |
| Pagination | 283 - 307 |
| Date Published | 02/2012 |
| ISSN | 1362-3087 |
| Keywords | Carnarvon Basin, multibeam acoustic, predictive modelling, seabed sediment |
| Abstract | Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam sonar mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. Multibeam acoustic data collected across a 1000 km2 area of the Carnarvon Shelf, Western Australia, were used in a predictive modelling approach to map eight seabed sediment parameters. Four machine learning models were used for the predictive modelling: boosted decision tree, random forest decision tree, support vector machine and generalised regression neural network. The results indicate overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness and Mean Grain Size. The study also demonstrates that predictive modelling using the combination of machine learning models has provided the ability to generate prediction uncertainty maps. However, the single models were shown to have overall better prediction performance than the combined models. Another important finding was that choosing an appropriate set of explanatory variables, through a manual feature selection process, was a critical step for optimising model performance. In addition, machine learning models were able to identify important explanatory variables, which are useful in identifying underlying environmental processes and checking predictions against the existing knowledge of the study area. The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of covariance of physical and biological data for this area. |
| URL | http://www.tandfonline.com/doi/abs/10.1080/13658816.2011.590139 |
| DOI | 10.1080/13658816.2011.590139 |
| Short Title | International Journal of Geographical Information Science |
