Imputation of evaporation data by using a support vector machine based model with limited meteorological data

Evaporation is a major factor in hydrological cycle. Its estimation can provide apractical reference for water resources management and agricultural irrigation. However, observed evaporation data are sometimes not available due to measurement or recording failure. In this research, an effective model based on support vector machine (SVM) is proposed to estimate missing pan evaporation by using meteorological data as input. First, the meteorological data that affect evaporation are collected, and the optimal input combination is selected by input determination process to construct SVM_opt model for evaporation estimation. Then, in order to extend the applicability of the proposed models, SVM_temp and SVM_hum models, which use commonly measured data in a weather station as input, are also constructed. Additionally, the proposed models are used to estimate missing evaporation data, and the estimation results are evaluated. Results show that the proposed models can estimate evaporation accurately with limited meteorological data, and the proposed models can estimate missing data consistently under different input combinations. The proposed modeling technique is expected to be useful to construct an evaporation estimation model, and the proposed model is recommended as an alternative approach for estimating missing evaporation data.