Forecasting reference evapotranspiration using data mining and limited climatic data

Abstract

To accurate forecast of water evaporation and transpiration (reference evapotranspiration, ET0) is imperative in the planning and management of water resources. The Penman-Monteith FAO56 (PM-56) equation which is recommended for estimating ET0 across the world. However, it requires several climatic variables; the use of the PM-56 equation is restricted by the unavailability of input climatic variables in many locations. In the current study, the potential of k-Nearest Neighbor algorithm (KNN), which is a data mining method for estimating ET0 were investigated using limited climatic data in a semi-arid environment in China. In addition, a KNN based ET0 forecast model were tested against the PM-56 equation. The accuracies of the models were evaluated by using three commonly used criteria: root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (r). The results obtained with the KNN-based ET0 forecast model (through normalization, weighted and K = 3) were better than it without any process. The prediction result is consistent with the PM-56 results, and confirmed the ability of these techniques to provide useful tools in ET0 modeling in semi-arid environments. Based on the comparison of the overall performances, it was found that t the KNN-based ET0 forecast model which requires max air temperature, min air temperature and relative humidity, input variables had the best accuracy.

Keywords

• reference evapotranspiration
• data mining
• k-nearest neighbor algorithm
• semi-arid environment