论文标题
GNSS位置时间序列的横向土地移动预测在机器学习辅助算法中
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm
论文作者
论文摘要
我们使用永久GNSS站的精确位置时间序列研究了常规机器学习辅助算法的准确性，以预测区域中横向陆地运动的准确性。所使用的机器学习算法与[1]中使用的算法差异，除了径向基函数，即多层perceptron，贝叶斯神经网络，高斯流程，k-nearest邻居，广义回归神经网络，分类和回归树，以及支持载体回归。对比较分析进行了比较分析，其中提到的机器学习方法的准确性相互检查。结果表明，时间序列的两个组成部分的最准确方法是高斯过程，精确度达到9.5厘米。
We investigate the accuracy of conventional machine learning aided algorithms for the prediction of lateral land movement in an area using the precise position time series of permanent GNSS stations. The machine learning algorithms that are used are tantamount to the ones used in [1], except for the radial basis functions, i.e. multilayer perceptron, Bayesian neural network, Gaussian processes, k-nearest neighbor, generalized regression neural network, classification and regression trees, and support vector regression. A comparative analysis is presented in which the accuracy level of the mentioned machine learning methods is checked against each other. It is shown that the most accurate method for both of the components of the time series is the Gaussian processes, achieving up to 9.5 centimeters in accuracy.
