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Jafari, Majid, Dinpashoh, Yaghob. (1398). Derivation of regression models for pan evaporation estimation. , 7(1), 29-42. doi: 10.22069/ijerr.2019.14302.1228
Majid Jafari; Yaghob Dinpashoh. "Derivation of regression models for pan evaporation estimation". , 7, 1, 1398, 29-42. doi: 10.22069/ijerr.2019.14302.1228
Jafari, Majid, Dinpashoh, Yaghob. (1398). 'Derivation of regression models for pan evaporation estimation', , 7(1), pp. 29-42. doi: 10.22069/ijerr.2019.14302.1228
Jafari, Majid, Dinpashoh, Yaghob. Derivation of regression models for pan evaporation estimation. , 1398; 7(1): 29-42. doi: 10.22069/ijerr.2019.14302.1228

Derivation of regression models for pan evaporation estimation

Environmental Resources Research
مقاله 3، دوره 7، شماره 1، مهر 2019، صفحه 29-42    اصل مقاله (2.1 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22069/ijerr.2019.14302.1228
نویسندگان
Majid Jafari* 1؛ Yaghob Dinpashoh2
1university of Tabriz, water resources management, Tabriz. IRAN
2Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Iran
چکیده
Evaporation is an essential component of hydrological cycle. Several meteorological
factors play role in the amount of pan evaporation. These factors are often related to each
other. In this study, a multiple linear regression (MLR) in conjunction with Principal
Component Analysis (PCA) was used for modeling of pan evaporation. After the
standardization of the variables, independent components were obtained using the (PCA).
The series of principal component scores were used as input in multiple linear regression
models. This method was applied to four stations in East Azerbaijan Province in the North
West of Iran. Mathematical models of pan evaporation were derived for each station. The
results showed that the first three components in all four stations account for more than
90% of the data variance. Performance criteria, namely coefficient of determination (R2)
and root mean square error (RMSE), were calculated for models in each station. The results
showed that in all the PCA-MLR models, the R2 value was greater than 0.74 (significant at
the 5% level) and the RMSE was less than 0.52 mm per day. In general, the results showed
an improvement in the results using combination of PCA and MLR models for pan
evaporation estimation.
کلیدواژه‌ها
Climatic data؛ East Azerbaijan؛ Pan evaporation؛ Principal component analysis؛ Regression models؛ PCA-MLR
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