Abstract
Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values.
Highlights
In recent years, with the in-depth development of information technology and the overall promotion of water conservancy, China’s water conservancy information construction has gradually deepened
Establish longitudinal predictive control model: use the test set to simulate the m model and weigh the combination of m models according to the test mean square error to establish the longitudinal predictive control model
The continuous hydrological data quality control method combines the above two consistency check prediction control models and adds regular QC methods in meteorological fields such as the format check, lack of test, the extreme value check, and the time-varying check to carry out comprehensive quality control for hydrological data
Summary
With the in-depth development of information technology and the overall promotion of water conservancy, China’s water conservancy information construction has gradually deepened. On the basis of research, the established hydrological data quality model proposed a hydrological data quality improvement scheme combining automatic cleaning and manual cleaning [8] and expounded the basic data quality processing methods from five aspects: missing data processing, logical error detection, repeated data processing, abnormal data detection, and inconsistent data processing, which provide some ideas for hydrological data quality control. Set the online real-time adjustment strategy, according to the seasonal variation characteristics hydrology,ofand the parameters the basic QC thresholds, Theofstructure thisdynamically paper is asadjust follows: Section 2 of introduces the parameters, research status and related and parameters of the predictive control model when establishing the control interval. On the basis of basic hydrological data are divided into two categories: continuous and discrete, which are checked and quality control (such as missing test and format check, extreme value check, and time-varying check), controlled separately.
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