论文标题
精确的机器学习辅助算法，用于土地沉降或GNSS时间序列的河流预测
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series
论文作者
论文摘要
本文旨在使用GNSS位置时间序列来预测某个区域的土地沉降或河流的问题。由于机器学习算法已将自己作为不同科学领域的强大预测工具表现出来，因此我们采用它们来预测GNSS位置时间序列的下一个值。因此，我们提出了一种算法，该算法利用机器学习算法来预测GNSS时间序列中的位置。所提出的算法具有两个步骤的预测和预测。在预处理阶段，删除了时间序列中的周期性潮汐和大气信号，并将坐标转移到局部坐标系。在预测阶段，使用了八种不同的机器学习算法，即多层感知器，贝叶斯神经网络，径向基础函数，高斯过程，k-nearest邻居，广义回归神经网络，分类和回归树和支持矢量回归。在14个不同的实际GNSS时间序列研究中，我们显示了高斯过程算法的优越性。所提出的算法的准确性最高为4毫米，平均精度为所有时间序列的2厘米。
This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. For this reason, we present an algorithm that takes advantage of the machine learning algorithms for the prediction of positions in a GNSS time series. The proposed algorithm has two steps-preprocessing and prediction. In the preprocessing phase, the periodic tidal and atmospheric signals in the time series are removed and coordinates are transferred to the local coordinate system. In the prediction phase, eight different machine learning algorithms are used, namely, multilayer perceptron, Bayesian neural network, radial basis functions, Gaussian processes, k-nearest neighbor, generalized regression neural network, classification and regression trees, and support vector regression. We show the superiority of the Gaussian processes algorithm, compared to other methods, in 14 different real GNSS time series studies. The proposed algorithm can achieve up to 4 millimeters in accuracy, with the average accuracy as 2 centimeters across all time series.
