Performance Comparison of Different Interpolation Methods on Missing Values for Time Series Data——A Case Study of Meteorological and Hydrological Data in Subtropical Small Watershed

doi:10.3969/j.issn.1000-6362.2018.03.007

Abstract

Abstract:

The effective estimation of the missing values of time series data at the scale of point process could improve its data quality. The meteorological and hydrological data sets (daily maximum air temperature, daily minimum air temperature, daily solar radiation, daily rainfall and daily stream flow) were collected through the long-term field experiments in a typically small subtropical watershed in subtropical zone. The performance differences within five interpolation methods of linear interpolation method(LIM), K-Nearest neighbor interpolation method(KNNM), spline interpolation method(SIM), polynomial interpolation method(PIM) and kernel density estimation method(KDEM) were analyzed on the above-mentioned five data sets. The root mean square error(RMSE), absolute mean error(MAE) and Pearson correlation coefficient(r) were selected to evaluate the advantages and disadvantages of the five methods. The results showed that: (1) The estimation performance of LIM, SIM and KDEM was generally superior to the other two methods. (2) The estimation of the missing values of meteorological data (maximum temperature, minimum temperature and solar radiation) produced the varying values of the three evaluation indices with RMSE values of 1.81-6.35, MAE values of 1.30-4.20 and r values of 0.70-0.98 (P＜0.05), respectively. In contrast, the estimation of missing values of hydrological data (rainfall and stream flow) had relatively high values of RMSE and MAE which were 12.51-26.28 and 3.60-14.21, respectively, and low values of r (0.07-0.72). So the above-mentioned interpolation methods generally produced better estimation of missing values of meteorological data sets than those of hydrological data. (3) Additionally, the coefficient of variation (CV) of the above data sets linearly correlated with the evaluation indices (RMSE, MAE and r) (P＜0.05), and played an important role in affecting the valuation performance of the above-mentioned interpolation methods.

Key words: Missing values, Interpolation methods, Coefficient of variance, Time series

GAN Lei, ZHOU Jiao-gen, SHI Jin, LI Xi, SHEN Jian-lin, LV Dian-qing, LI Yu-yuan,WU Jin-shui. Performance Comparison of Different Interpolation Methods on Missing Values for Time Series Data——A Case Study of Meteorological and Hydrological Data in Subtropical Small Watershed[J]. Chinese Journal of Agrometeorology, 2018, 39(03): 195-204.

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