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.