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In summary, this study established the ANN and WNN models based on two parameters (N and RH), extracted the corresponding parameters of network structure, chose the city of Guangzhou as the benchmark station with the highest cumulative Rdc among all stations, and applied the data for 2004–2010 from other stations to verify and universally analyse the models. The universal results from the ANN and WNN models are presented in Tables 6 and 7, respectively.

Table 6

Comparison of ET0 estimated errors based on ANN model at the capital stations in southern China

StationCumulative RdcRMSE (mm)MAE (mm)MAPE (%)NSELinear regression equationR2
Guangzhou 0.9044 0.1415 0.1121 2.6955 0.9660 y = 0.9854x − 0.0014 0.9717 
Nanning 0.8820 0.1383 0.1023 2.4493 0.9657 y = 1.0201x − 0.0976 0.9664 
Kunming 0.8785 0.1361 0.1113 3.2502 0.8182 y = 0.8632x + 0.0783 0.9531 
Haikou 0.8713 0.1590 0.1239 2.6204 0.9399 y = 1.0861x − 0.2938 0.9615 
Guiyang 0.8567 0.1511 0.1192 3.5607 0.8316 y = 0.9297x − 0.0161 0.9627 
Chongqing 0.8490 0.1922 0.1423 3.7743 0.9683 y = 1.0624x − 0.2981 0.9734 
Fuzhou 0.8382 0.2454 0.1845 3.8932 0.8801 y = 1.1862x − 0.5786 0.9568 
Changsha 0.8293 0.2382 0.1844 3.9798 0.9329 y = 1.1785x − 0.7627 0.9608 
Hangzhou 0.8258 0.2767 0.2076 4.7596 0.9204 y = 1.1532x − 0.5945 0.9433 
Shanghai 0.8125 0.3131 0.2393 5.3582 0.8629 y = 1.1932x − 0.6010 0.9281 
Nanchang 0.8105 0.2568 0.2000 4.1421 0.9264 y = 1.1557x − 0.7046 0.9459 
Wuhan 0.8094 0.2583 0.2057 4.8053 0.9175 y = 1.0999x − 0.5577 0.9344 
StationCumulative RdcRMSE (mm)MAE (mm)MAPE (%)NSELinear regression equationR2
Guangzhou 0.9044 0.1415 0.1121 2.6955 0.9660 y = 0.9854x − 0.0014 0.9717 
Nanning 0.8820 0.1383 0.1023 2.4493 0.9657 y = 1.0201x − 0.0976 0.9664 
Kunming 0.8785 0.1361 0.1113 3.2502 0.8182 y = 0.8632x + 0.0783 0.9531 
Haikou 0.8713 0.1590 0.1239 2.6204 0.9399 y = 1.0861x − 0.2938 0.9615 
Guiyang 0.8567 0.1511 0.1192 3.5607 0.8316 y = 0.9297x − 0.0161 0.9627 
Chongqing 0.8490 0.1922 0.1423 3.7743 0.9683 y = 1.0624x − 0.2981 0.9734 
Fuzhou 0.8382 0.2454 0.1845 3.8932 0.8801 y = 1.1862x − 0.5786 0.9568 
Changsha 0.8293 0.2382 0.1844 3.9798 0.9329 y = 1.1785x − 0.7627 0.9608 
Hangzhou 0.8258 0.2767 0.2076 4.7596 0.9204 y = 1.1532x − 0.5945 0.9433 
Shanghai 0.8125 0.3131 0.2393 5.3582 0.8629 y = 1.1932x − 0.6010 0.9281 
Nanchang 0.8105 0.2568 0.2000 4.1421 0.9264 y = 1.1557x − 0.7046 0.9459 
Wuhan 0.8094 0.2583 0.2057 4.8053 0.9175 y = 1.0999x − 0.5577 0.9344 
Table 7

Comparison of ET0 estimated errors based on WNN model at the capital stations in southern China

StationCumulative RdcRMSE (mm)MAE (mm)MAPE (%)NSELinear regression equationR2
Guangzhou 0.9044 0.1526 0.1168 2.8875 0.9671 y = 0.9888x − 0.0177 0.9731 
Nanning 0.8820 0.1430 0.1060 2.5419 0.9641 y = 1.0217x − 0.1087 0.9652 
Kunming 0.8785 0.3439 0.3042 8.9030 0.7007 y = 0.8749x + 0.1606 0.9539 
Haikou 0.8713 0.1979 0.1426 2.9153 0.9404 y = 1.0832x − 0.2850 0.9604 
Guiyang 0.8567 0.3245 0.2831 8.5391 0.8282 y = 0.9359x − 0.0418 0.9609 
Chongqing 0.8490 0.2192 0.1629 4.4129 0.9654 y = 1.0765x − 0.3550 0.9720 
Fuzhou 0.8382 0.4271 0.3309 6.2844 0.8691 y = 1.2109x − 0.6815 0.9543 
Changsha 0.8293 0.3890 0.2933 5.7415 0.8954 y = 1.2559x − 1.0575 0.9509 
Hangzhou 0.8258 0.3561 0.2692 5.7362 0.9061 y = 1.1898x − 0.7432 0.9380 
Shanghai 0.8125 0.4500 0.3357 6.6770 0.8515 y = 1.2088x − 0.6632 0.9214 
Nanchang 0.8105 0.3544 0.2663 5.2477 0.8971 y = 1.2107x − 0.9211 0.9330 
Wuhan 0.8094 0.3114 0.2374 5.5929 0.9046 y = 1.1399x − 0.7108 0.9232 
StationCumulative RdcRMSE (mm)MAE (mm)MAPE (%)NSELinear regression equationR2
Guangzhou 0.9044 0.1526 0.1168 2.8875 0.9671 y = 0.9888x − 0.0177 0.9731 
Nanning 0.8820 0.1430 0.1060 2.5419 0.9641 y = 1.0217x − 0.1087 0.9652 
Kunming 0.8785 0.3439 0.3042 8.9030 0.7007 y = 0.8749x + 0.1606 0.9539 
Haikou 0.8713 0.1979 0.1426 2.9153 0.9404 y = 1.0832x − 0.2850 0.9604 
Guiyang 0.8567 0.3245 0.2831 8.5391 0.8282 y = 0.9359x − 0.0418 0.9609 
Chongqing 0.8490 0.2192 0.1629 4.4129 0.9654 y = 1.0765x − 0.3550 0.9720 
Fuzhou 0.8382 0.4271 0.3309 6.2844 0.8691 y = 1.2109x − 0.6815 0.9543 
Changsha 0.8293 0.3890 0.2933 5.7415 0.8954 y = 1.2559x − 1.0575 0.9509 
Hangzhou 0.8258 0.3561 0.2692 5.7362 0.9061 y = 1.1898x − 0.7432 0.9380 
Shanghai 0.8125 0.4500 0.3357 6.6770 0.8515 y = 1.2088x − 0.6632 0.9214 
Nanchang 0.8105 0.3544 0.2663 5.2477 0.8971 y = 1.2107x − 0.9211 0.9330 
Wuhan 0.8094 0.3114 0.2374 5.5929 0.9046 y = 1.1399x − 0.7108 0.9232 

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