Abstract
This study evaluates neural networks models for estimating daily pan evaporation for inland and coastal stations in Republic of Korea. A multilayer perceptron neural networks model (MLP-NNM) and a cascade correlation neural networks model (CCNNM) are developed for local implementation. Five-input models (MLP 5 and CCNNM 5) are generally found to be the best for local implementation. The optimal neural networks models, including MLP 4, MLP 5, CCNNM 4, and CCNNM 5, perform well for homogeneous (cross-stations 1 and 2) and nonhomogeneous (cross-stations 3 and 4) weather stations. Statistical results of CCNNM are better than those of MLP-NNM during the test period for homogeneous and nonhomogeneous weather stations except for MLP 4 being better in BUS-DAE and POH-DAE, and MLP 5 being better in POH-DAE. Applying the conventional models for the test period, it is found that neural networks models perform better than the conventional models for local, homogeneous, and nonhomogeneous weather stations.
Similar content being viewed by others
References
Adeli H, Hung SL (1995) Machine learning neural networks, genetic algorithms, and fuzzy systems. John Wiley & Sons Inc., New York
Bruton JM, McClendon RW, Hoogenboom G (2000) Estimating daily pan evaporation with artificial neural networks. Trans ASAE 43(2):491–496
Burman RD (1976) Intercontinental comparison of evaporation estimates. ASCE J Irrig Drain Eng 102(1):109–118
Chang FJ, Chang LC, Kao HS, Wu GR (2010) Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. J Hydrol 384(1–2):118–129
Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, New York
Coulomb CV, Legesse D, Gasse F, Travi Y, Chernet T (2001) Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). J Hydrol 245(1–4):1–18
de Ridder NA, Boonstra J (1994) Analysis of water balances. In: Ritzema HP (ed) Drainage principles and applications, 2nd edn, International Institute for Land Reclamation and Improvement (ILRI). Wageningen, The Netherlands, pp 601–633
Fahlman SE, Lebiere C (1990) The cascade correlation learning architecture. Rep. CMU-CS-90-100, Carnegie Mellon University, Pittsburgh
Fitch JP, Lehman SK, Dowla FU, Lu SY, Johansson EM, Goodman DM (1991) Ship wake-detention procedure using conjugate gradient trained artificial neural networks. IEEE Trans Geosci Remote Sens 29(5):718–726
Fletcher R, Reeves CM (1964) Function minimization by conjugate gradients. Comput J 7:149–153
Gavin H, Agnew CA (2004) Modeling actual, reference and equilibrium evaporation from a temperate wet grassland. Hydrol Process 18(2):229–246
Griffiths JF (1966) Another evaporation formula. Agr Meteorol 3(3–4):257–261
Guven A, Kisi O (2011) Daily pan evaporation modeling using linear genetic programming technique. Irrig Sci 29(2):135–145
Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. ASCE J Comput Civ Eng 8(2):201–220
Kay AL, Davies HN (2008) Calculating potential evaporation from climatic data: a source of uncertainty for hydrological climate changes impact. J Hydrol 358(3–4):221–239
Keskin ME, Terzi O (2006) Artificial neural networks models of daily pan evaporation. ASCE J Hydrol Eng 11(1):65–70
Kim S, Kim JH, Park KB (2009) Statistical learning theory for the disaggregation of the climatic data. In: Proceedings of 33rd IAHR Congress 2009, IAHR, Vancouver, Canada, pp. 1154–1162
Kim S, Shiri J, Kisi O (2012) Pan evaporation modeling using neural computing approach for different climatic zones. Water Resour Manag 26(11):3231–3249
Kim S, Shiri J, Kisi O, Singh VP (2013) Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resour Manag 27(7):2267–2286
Kisi O (2006) Daily pan evaporation modeling using a neuro-fuzzy computing technique. J Hydrol 329(3–4):636–646
Kisi O (2007) Streamflow forecasting using different artificial neural network algorithm. ASCE J Hydrol Eng 12(5):532–539
Kisi O (2009) Modeling monthly evaporation using two different neural computing technique. Irrig Sci 27(5):417–430
Kisi O, Pour-Ali Baba A, Shiri J (2012) Generalized neurofuzzy models for estimating daily pan evaporation values from weather data. ASCE J Irrig Drain Eng 138(4):349–362
Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural networks. ASCE J Irrig Drain Eng 128(4):224–233
Kumar M, Raghuwanshi NS, Singh R (2011) Artificial neural networks approach in evapotranspiration modeling: a review. Irrig Sci 29(1):11–25
Linarce ET (1967) Climate and evaporation from crops. ASCE J Irrig Drain Eng 93(4):61–79
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, part 1—a discussion of principles. J Hydrol 10(3):282–290
Penman WR (1948) Natural evaporation from open water, bare soil and grass. Proc Royal Soc Edinb Sect A Math 193(1032):120–145
Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92
Reis RJ, Dias NL (1998) Multi-season lake evaporation: energy budget estimates and CRLE model assessment with limited meteorological observations. J Hydrol 208(3–4):135–147
Shiri J, Kisi O (2011) Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (Southwestern Iran). ASCE J Irrig Drain Eng 137(7):412–425
Shiri J, Dierickx W, Pour-Ali Baba A, Neamati S, Ghorbani MA (2011) Estimating daily pan evaporation from climatic data of the state of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrol Res 42(6):491–502
Shirsath PB, Singh AK (2010) A comparative study of daily pan evaporation estimation using ANN, regression and climate based models. Water Resour Manag 24(8):1571–1581
Simpson PK (1990) Artificial neural systems: foundations, paradigms, applications and implementations. Pergamon, New York
Singh VP (1988) Hydrologic system rainfall–runoff modelling, vol 1. Prentice Hall, New Jersey
Sivakumar B, Jayawardena AW, Fernando TMKG (2002) River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. J Hydrol 265:225–245
Sudheer KP, Gosain AK, Rangan DM, Saheb SM (2002) Modeling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202
Tabari H, Marofi S, Sabziparvar AA (2009) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28(5):399–406
Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. ASCE J Hydrol Eng 3(1):26–32
Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. ASCE J Hydrol Eng 4(3):232–239
Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering. John Wiley & Sons Inc, New York
Wasserman PD (1993) Advanced methods in neural computing. Van Nostrand Reinhold, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, S., Singh, V.P. & Seo, Y. Evaluation of pan evaporation modeling with two different neural networks and weather station data. Theor Appl Climatol 117, 1–13 (2014). https://doi.org/10.1007/s00704-013-0985-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-013-0985-y