The effect of data quality on model performance with application to daily evaporation estimation

作者：Wu Ming Chang; Lin Gwo Fong^*; Lin Hsuan Yu

来源：Stochastic Environmental Research and Risk Assessment, 2013, 27(7): 1661-1671.

DOI：10.1007/s00477-013-0703-4

• Data quality
• Missing data
• Optimal input combination
• Daily evaporation
• Meteorological data
• Support vector machine

The model performance is usually influenced by the quality of the data used in model construction. If the model performance is less affected by data quality, the resulting estimates will be more reliable. In this paper, the variation in model performance due to different data quality is explored in a field-scale application. Hence, two models, the proposed support vector machine (SVM) based model and the Stephen and Stewart (SS) model, are employed for daily estimation of evaporation at an experiment station. Five scenarios corresponding to different data qualities are designed to evaluate the effect of data quality on model performance. Additionally, the most effective meteorological variables influencing evaporation are obtained by a systematic input determination process. These most effective meteorological variables are used as inputs to the SVM-based model. The results show that the model performance decreases as the data quality decreases (i.e. the percentage of missing data increases). However, the estimation accuracy of SVM-based models is still better than that of the SS model. Moreover, the variation of model performance of the SVM-based model is smaller than that of the SS model. That is, the negative impact of different data quality is effectively decreased by using the SVM-based model instead of the SS model.

• 出版日期2013-10