A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations

Ye, Xiaoling; Kan, Yuanliang; Xiong, Xiong; Zhang, Yingchao; Chen, Xin

doi:10.1155/2020/6045492

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Alghamdi et al 6 designed the MA chart for the Weibull distribution. Ye et al 7 applied quality control methods for air data. Su et al 8 used the machine learning technique for metrology data.…”

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confidence: 99%

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Aslam

Shareef

Khan

2020

Sci Rep

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the existing moving average control charts can be only applied when all observations in the data are determined, precise, and certain. But, in practice, the data from the weather monitoring is not exact and express in the interval. in this situation, the available monitoring plans cannot be applied for the monitoring of weather data. A new moving average control chart for the normal distribution is offered under the neutrosophic statistics. The parameters of the offered chart are determined through simulation under neutrosophic statistics. the comparison study shows the superiority of the proposed chart over the moving average control chart under classical statistics. A real example from the weather is chosen to present the implementation of the chart. from the simulation study and real data, the proposed chart is found to be effective to be applied for temperature monitoring than the existing control chart.

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confidence: 99%

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Aslam

Shareef

Khan

2020

Sci Rep

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“…Although the traditional radiosonde data have high representativeness and reliability, the traditional observations are expensive and lack spatiotemporal resolution [7]. Ground-based microwave radiometers (MWRs) with passive remote sensing technology can overcome these shortcomings [8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer

Luo

Gu³

et al. 2023

Remote Sensing

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The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.

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“…Considering the limitations of current QC methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the QC of surface air temperature observations. Ye et al (2020) improved the kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization. The RainGaugeQC scheme described in this study aims to provide realtime QC of the telemetric rain gauge data.…”

Section: Introductionmentioning

confidence: 99%

Quality control algorithm of wind speed monitoring data along high-speed railway

Xiong

Cui³

et al. 2023

Front. Energy Res.

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For studying the traffic safety of the high-speed railway, this research considers high-quality second-level wind speed data as its basis. However, the quality of second-level wind speed data can be greatly lowered by disturbances during data collection and storage. Therefore, it is crucial to control the data quality during collection and storage. Wind speed data along the high-speed railway are unstable and non-linear. In order to adapt to this characteristic, this study combines a convolutional neural network (CNN), long short-term memory (LSTM), and isolated forest from the time dimension to form a quality control (QC) algorithm for wind speed monitoring data. First, CNN is used to extract the original data features, which are then transferred to the LSTM network for one-step prediction. The prediction residual of the model is obtained and sent to the isolated forest, where the abnormal value position in the original wind speed data is calibrated by detecting the abnormal value position in the prediction residual. Comparative experiments have been conducted to test the performances of the three different QC methods. The results show that the error detection rate of CNN–LSTM–IF in this research method is approximately 0.95. For different terrains and seasons, the method has certain robustness and generalization.

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A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations

Cited by 4 publications

References 13 publications

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer

Quality control algorithm of wind speed monitoring data along high-speed railway

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