The accuracy of spatially continuous environmental data, usually generated from point samples using spatial prediction methods, is crucial for evidence-informed environmental management and conservation. Improving the accuracy by identifying the most accurate methods is essential, but also challenging since the accuracy is often data specific and affected by multiple factors. Recently developed hybrid methods of machine learning methods and geostatistics have shown their advantages in spatial predictive modelling in environmental sciences and significantly improved predictive accuracy. An R package, ‘spm: Spatial Predictive Modelling’, has been developed to introduce these methods and has been recently released for R users. It not only introduces the hybrid methods for improving predictive accuracy, but can also be used to improve modelling efficiency. This presentation will briefly introduce the developmental history of novel hybrid geostatistical and machine learning methods in spm. It will introduce spm, by covering: 1) spatial predictive methods, 2) new hybrid methods of geostatistical and machine learning methods, 3) assessment of predictive accuracy, 4) applications of spatial predictive models, and 5) relevant functions in spm. It will then demonstrate how to apply some functions in spm to relevant datasets and to show the resultant improvement in predictive accuracy and modelling efficiency. Although in this presentation, spm is applied to data in environmental sciences, it can be applied to data in other relevant disciplines. Presentation at the 2018 useR! conference

Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry related variables due to the lack of backscatter related data. In this study, we applied random forests (RF), 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 the most accurate model to predict seabed sand content at local scale, by also addressing 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 can significantly improve predictive accuracy and are recommended; 6) relationships of 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 for environmental management and conservation in the study region. <b>Citation:</b> 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. <i>Geosciences</i> 2019, 9, 180. https://doi.org/10.3390/geosciences9040180