ABSTRACT

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. This study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R² (coefficient of determination) increases with depth with a maximum R² of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

摘要

卫星遥感已广泛用于绘制水体中的悬浮沉积物浓度（SSC）。但是，由于沉积物-水相互作用的复杂性，很难推导出线性和非线性回归方程来可靠地预测南南合作，特别是在尝试估算整体沉积物深度时。这项研究使用Landsat 8 OLI（可操作的土地成像仪）传感器在多个深度间隔（15、61、91和182 cm）对美国俄亥俄州莫米河内的SSC作图。简单的线性最小二乘回归（LLSR）和三种常见的机器学习模型：随机森林（RF），支持向量回归（SVR）和模型平均神经网络（MANN）用于在深度间隔处估计SSC。所有机器学习模型均明显优于LLSR，而RF表现最佳。在RF和MANN中，R ²（测定系数）随着深度的增加而增加，在0–182 cm的深度处，最大R ²分别为0.89和0.83。结果表明，在估计深度积分SSC时，机器学习模型可以实现比传统的线性回归方法产生更好的预测的非线性关系，尤其是在样本有限的情况下。