عنوان مقاله [English]
Evapotranspiration is the most important part of the hydrological cycle, which plays a key role in water resource management, crop yield simulation, and irrigation scheduling. The purpose of this research was to estimate the reference evapotranspiration using ‘panel-data’ models. Panel-data multivariate analysis endows regression analysis with both spatial and temporal dimensions. This study was carried out using weather data of 9 synoptic stations of Khorasan Razavi during 1971-2000. Data were divided randomly into two sub-sets, 75% for model development and 25% for model evaluation. The panel-data models were developed using the monthly mean air temperature and monthly mean wind speed as inputs in order to estimate monthly reference evapotranspiration. The results indicated that the two-way fixed effects models were superior. The statistical index (RMSE = 9.85, MAE = 7.38 and R2 = 0.99) revealed the effectiveness of this model. In addition, these results were compared with the results of ordinary least squares regression and Hargreaves-Samani equation which showed the superiority of the panel-data models.
کواکبی، غ.، موسوی بایگی، م.، مساعدی، ا.، جباری نوقابی، م.، 1393. تعیین عوامل موثر بر وقوع خشکسالی با تحلیل داده های پانلی (مطالعه موردی استان خراسان رضوی). نشریه آب و خاک (علوم و صنایع کشاورزی) 6: 1298–1310.
Eslamian, S.S., Gohari, S.A., Zareian, M.J., Firoozfar, A., 2012. Estimating Penman–Monteith reference evapotranspiration using artificial neural networks and genetic algorithm: a case study. Arabian Journal for Science and Engineering. 37. 4: 935–944.
Feng, Y., Cui, N., Gong, D., Zhang, Q., Zhao, L., 2017. Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management. 193: 163–173.
Izady, A., Davary, K., Alizadeh, A., Ghahraman, B., Sadeghi, M., Moghaddamnia, A., 2012. Application of “panel-data” modeling to predict groundwater levels in the Neishaboor Plain, Iran. Hydrogeology Journal. 20. 3: 435–447.
Shiri, J., Nazemi, A.H., Sadraddini, A.A., Landeras, G., Kisi, O., Fard, A.F., Marti, P., 2013. Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration. Journal of Hydrology. 480: 46–57.