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首页> 外文期刊>Geosciences >Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods
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Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

机译：使用机器学习，地统计学及其混合方法开发海床含沙量的最佳空间预测模型

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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.
机译：由于缺乏反向散射数据，澳大利亚在区域和国家范围内对海底沉积物的预测主要基于与测深法相关的变量。在这项研究中，我们将随机森林（RF），RF和地统计学的混合方法以及广义增强回归建模（GBM）应用于海床砂含量点数据和声多波束数据及其派生变量，以开发出准确的模型来进行预测局部规模的海底砂含量。我们还通过变量选择解决了相关问题。发现：（1）与反向散射相关的变量比测深相关的变量在砂粒预测建模中更为重要; （2）包含高度相关的预测变量可以提高预测准确性; （3）平均变量重要性（AVI）的等级顺序和准确性贡献随RF输入预测变量而变化，并且不一定匹配; （4）建议将知识型AVI方法（KIAVI2）用于RF; （5）混合方法及其平均可以显着提高预测准确性，值得推荐; （6）沙子与预测变量之间的关系是非线性的; （7）GBM变量选择方法有待进一步研究。可以高精度地预测含沙量，从而为环境管理和保护提供重要的基线信息。

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