Fig 1.
The distribution of different climatic regions across China and the geographical locations of 14 weather stations used in this study.
NWC: arid desert of northwest China, IM: semi-arid steppe of Inner Mongolia, QTP: Qinghai-Tibetan Plateau, NEC: (semi-)humid cold-temperate northeast China, NC: semi-humid warm-temperate north China, CC: humid subtropical central China, and SC: humid tropical south China; the South China Sea Islands are presented in the bottom right-hand corner; the same below.
Table 1.
The geographical locations and annual mean values (± standard deviation) of meteorological data during 2001–2015 for each of the 14 weather stations used in the present study.
Fig 2.
Simple flowchart of the proposed methodology in the present study.
Table 2.
The five cross-validation stages involved in the present study.
Table 3.
Statistical values of the eight machine learning models with different input parameters during training and testing at Urumqi and Dunhuang in the arid desert of northwest China.
Table 4.
Statistical values of the eight machine learning models with different input parameters during training and testing at Yinchuan and Erenhot in the semi-arid steppe of Inner Mongolia.
Table 5.
Statistical values of the eight machine learning models with different input parameters during training and testing at Harbin and Shenyang in the (semi-)humid cold-temperate northeast China.
Table 6.
Statistical values of the eight machine learning models with different input parameters during training and testing at Beijing and Zhengzhou in the semi-humid warm-temperate north China.
Table 7.
Statistical values of the eight machine learning models with different input parameters during training and testing at Geermu and Lasa in the Qinghai-Tibetan Plateau.
Table 8.
Statistical values of the eight machine learning models with different input parameters during training and testing at Wuhan and Guilin in the humid subtropical central China.
Table 9.
Statistical values of the eight machine learning models with different input parameters during training and testing at Guangzhou and Haikou in the humid tropical south China.
Fig 3.
Scatter plots of the ET0 values calculated by the FAO-56 PM equation for China’s capital city of Beijing and the values estimated by the eight machine learning models during five cross-validation stages under the input combination of Tmax, Tmin and Ra in the testing stage.
Fig 4.
Scatter plots of the ET0 values calculated by the FAO-56 PM equation for China’s capital city of Beijing and the values estimated by the eight machine learning models during five cross-validation stages under the input combination of Tmax, Tmin, Pt and Ra in the testing stage.
Fig 5.
Scatter plots of predicted ET0 values using the eight machine learning models against their corresponding FAO56-PM values during testing at Urumqi and Dunhuang in the arid desert of northwest China.
Fig 6.
Scatter plots of predicted ET0 values using the eight machine learning models against their corresponding FAO56-PM values during testing at Guangzhou and Haikou in the humid tropical south China.