Comparison of artificial neural networks and empirical equations to estimate daily pan evaporation

2008 ◽  
pp. n/a-n/a ◽  
Author(s):  
Özlem Terzi ◽  
M. Erol Keskin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Equations ◽  
Pan Evaporation ◽  
Daily Pan Evaporation ◽  
Artificial Neural

ESTIMATING DAILY PAN EVAPORATION WITH ARTIFICIAL NEURAL NETWORKS

2000 ◽  
Vol 43 (2) ◽  
pp. 491-496 ◽  
Author(s):  
J. M. Bruton ◽  
R. W. McClendon ◽  
G. Hoogenboom
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pan Evaporation ◽  
Daily Pan Evaporation ◽  
Artificial Neural

scholarly journals Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1508 ◽  
Author(s):  
Abdullah A. Alsumaiei
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Climatic Conditions ◽  
Pan Evaporation ◽  
Wind Speeds ◽  
Air Temperatures ◽  
Management Plans ◽  
Ann Models ◽  
Artificial Neural ◽  
The State Of Kuwait

Evaporation is the major water-loss component of the hydrologic cycle and thus requires efficient management. This study aims to model daily pan evaporation rates in hyper-arid climates using artificial neural networks (ANNs). Hyper-arid climates are characterized by harsh environmental conditions where annual precipitation rates do not exceed 3% of annual evaporation rates. For the first time, ANNs were applied to model such climatic conditions in the State of Kuwait. Pan evaporation data from 1993–2015 were normalized to a 0–1 range to boost ANN performance and the ANN structure was optimized by testing various meteorological input combinations. Levenberg–Marquardt algorithms were used to train the ANN models. The proposed ANN was satisfactorily efficient in modeling pan evaporation in these hyper-arid climatic conditions. The Nash–Sutcliffe coefficients ranged from 0.405 to 0.755 over the validation period. Mean air temperatures and average wind speeds were identified as meteorological variables that most influenced the ANN performance. A sensitivity analysis showed that the number of hidden layers did not significantly impact the ANN performance. The ANN models demonstrated considerable bias in predicting high pan evaporation rates (>25 mm/day). The proposed modeling method may assist water managers in Kuwait and other hyper-arid regions in establishing resilient water-management plans.

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