A strategy of singular value identification and correction for water monitoring data

ZHANG Feng, XUE Huifeng, SONG Xiaona, WAN Yi

Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (7) : 1867-1876.

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Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (7) : 1867-1876. DOI: 10.12011/1000-6788-2018-0044-10

A strategy of singular value identification and correction for water monitoring data

  • ZHANG Feng1,2, XUE Huifeng2, SONG Xiaona1,3, WAN Yi4
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Abstract

The improvement of water resources monitoring data quality is an important content of the national water resources monitoring capacity building project. Hence, according to the actual statistical situation of national water resources monitoring data, the method of wavelet transform modulus maxima was applied to the noise reduction of water monitoring data and its singular value identification, and then the singular value was removed so that a new time-series monitoring data sequence could be corrected. This data sequence was used as the training samples of the least squares support vector machine model optimized by article swarm optimization (PSO-LSSVM), and singular value would be corrected by the fitting function of PSO-LSSVM model. All of above methods were tested through an empirical case of water monitoring data. Results showed that the original information of water monitoring data could be kept as much as possible using the method of wavelet transform modulus maxima, because this method improved the separation of high frequency and low frequency information, so it could reduce noise effectively and observe the inherent changes in water monitoring data. Meanwhile, the singular values were excavated in water resources monitoring data based on the method of wavelet transform modulus maxima, and also its application effect was better than traditional statistical method. The sample fitting accuracy of PSO-LSSVM model was higher than LSSVM and curve fitting, so the singular value was reconstructed by PSO-LSSVM model, and these reconstructed data were consistent with the objective law of actual water demand.

Key words

water monitoring / singular value / wavelet transform / least squares support vector machine

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ZHANG Feng , XUE Huifeng , SONG Xiaona , WAN Yi. A strategy of singular value identification and correction for water monitoring data. Systems Engineering - Theory & Practice, 2019, 39(7): 1867-1876 https://doi.org/10.12011/1000-6788-2018-0044-10

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Funding

National Natural Science Foundation of China (U1501253); Guangdong Provincial Science and Technology Project (2016B010127005)
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