Department of Physics Chehla Campus, University of Azad Jammu and Kashmir Muzaffarbad, 13100, Azad Kashmir, Pakistan.
Department of Computer Science and Information Technology, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
Sci Rep. 2020 Feb 20;10(1):3004. doi: 10.1038/s41598-020-59881-9.
We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g., Extreme Gradient Boosting (EGB) and simple regression methods e.g., support vector regressors with linear kernel and radial kernel in terms of accurate predictions. R language has been used for the statistical analysis of radon time series (RTS) data. The results obtained show that the proposed methodology predicts SRGC more accurately when compared to different traditional boosting and regression methods. The best correlation is found between the actual and predicted radon concentration for window size of 2 i.e., two days before and after the start of seismic activities. RTS data was collected from 05 February 2017 to 16 February 2018, including 7 seismic events recorded during the study period. Findings of study show that the proposed methodology predicts the SRGC with more precision, for all the window sizes, by overlapping predicted with the actual radon time series concentrations.
我们提出了一种新的方法，该方法基于委托回归器的思想，用于预测土壤氡气浓度（SRGC）和氡或任何其他时间序列数据中的异常。在所提出的方法中，与不同的传统提升方法（例如，极端梯度提升（EGB））和简单回归方法（例如，具有线性核和径向核的支持向量回归器）进行了比较，就准确预测而言。已经使用 R 语言对氡时间序列（RTS）数据进行了统计分析。所得结果表明，与不同的传统提升和回归方法相比，所提出的方法可以更准确地预测 SRGC。在地震活动开始前和后两天的窗口大小为 2 时，发现实际和预测的氡浓度之间的相关性最佳。RTS 数据是在 2017 年 2 月 5 日至 2018 年 2 月 16 日之间收集的，包括研究期间记录的 7 次地震事件。研究结果表明，该方法通过将预测值与实际氡时间序列浓度重叠，以更高的精度预测了所有窗口大小的 SRGC。
