Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content for the AEEZ using samples extracted on August 2010 from Geoscience Australia's Marine Samples Database. The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region but the choice of mtry. No single method performs best for all the tested scenarios. Of the 18 compared methods, RFIDS and RFOK are the most accurate methods in all three regions. Overall, of the 36 combinations of input secondary variables, methods and regions, RFIDS, 6RFIDS and RFOK were among the most accurate methods in all three regions. Model averaging further improved the prediction accuracy. The most accurate methods reduced the prediction error by up to 7%. RFOKRFIDS, with a search window size of 5, an mtry of 4 and more realistic predictions in comparison with the control, is recommended for predicting sand content across the AEEZ if a single method is required. This study provides suggestions and guidelines for improving the spatial interpolations of marine environmental data.

本研究旨在利用2010年8月从澳大利亚地质调查局（Geoscience Australia）海洋样本数据库（Marine Samples Database）中提取的样本，筛选适用于澳大利亚专属经济区（Australian Exclusive Economic Zone, AEEZ）海底砂含量空间插值（spatial interpolation）的最优方法。预测精度会随所采用的方法、输入辅助变量、模型平均策略（model averaging）、搜索窗口大小以及研究区域的不同而变化，但不受mtry参数选择的影响。不存在一种方法可在所有测试场景中取得最优表现。在18种对比测试的方法中，RFIDS与RFOK在三个研究区域内均表现出最高的预测精度。总体而言，在输入辅助变量、方法与研究区域的36种组合方案中，RFIDS、6RFIDS与RFOK均跻身三个区域内精度最优的方法之列。模型平均策略可进一步提升预测精度。精度最优的方法可将预测误差最高降低7%。若仅需采用单一方法开展澳大利亚专属经济区全域的海底砂含量预测，推荐采用搜索窗口大小为5、mtry参数为4且相较于基准对照方法（control）预测结果更贴合实际的RFOK与RFIDS方法。本研究可为海洋环境数据空间插值方法的优化提供参考与指导准则。