caa a22 4500 445837691 CHVBK 20180317145334.0 cr unu---uuuuu 170323e20111101xx s 000 0 eng 10.1007/s00271-010-0254-0 doi (NATIONALLICENCE)springer-10.1007/s00271-010-0254-0 Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration [Elektronische Daten] [Coskun Ozkan, Ozgur Kisi, Bahriye Akay] The study investigates the ability of artificial neural networks (ANN) with artificial bee colony (ABC) algorithm in daily reference evapotranspiration (ET0) modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN-ABC model so as to estimate ET0 obtained using the FAO-56 Penman-Monteith (PM) equation. In the first part of the study, the accuracy of ANN-ABC models is compared with those of the ANN models trained with Levenberg-Marquardt (LM) and standard back-propagation (SBP) algorithms and those of the following empirical models: The California Irrigation Management System (CIMIS) Penman, Hargreaves, and Ritchie methods. The mean square error (MSE), mean absolute error (MAE) and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Based on the comparison results, the ANN-ABC and ANN-LM models are found to be superior alternative to the ANN-SBP models. In the second part of the study, the potential of the ANN-ABC, ANN-LM, and ANN-SBP models in estimation ET0 using nearby station data is investigated. Springer-Verlag, 2010 Ozkan Coskun Engineering Faculty, Geomatics Department, Erciyes University, Kayseri, Turkey aut Kisi Ozgur Engineering Faculty, Civil Engineering Department, Erciyes University, Kayseri, Turkey aut Akay Bahriye Engineering Faculty, Computer Science Department, Erciyes University, Kayseri, Turkey aut Irrigation Science Springer-Verlag 29/6(2011-11-01), 431-441 0342-7188 29:6<431 2011 29 271 https://doi.org/10.1007/s00271-010-0254-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00271-010-0254-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ozkan Coskun Engineering Faculty, Geomatics Department, Erciyes University, Kayseri, Turkey aut NATIONALLICENCE 700 1- Kisi Ozgur Engineering Faculty, Civil Engineering Department, Erciyes University, Kayseri, Turkey aut NATIONALLICENCE 700 1- Akay Bahriye Engineering Faculty, Computer Science Department, Erciyes University, Kayseri, Turkey aut NATIONALLICENCE 773 0- Irrigation Science Springer-Verlag 29/6(2011-11-01), 431-441 0342-7188 29:6<431 2011 29 271 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer