Differential evolution based radial basis function neural network model for reference evapotranspiration estimation

نویسندگان

چکیده

Abstract The present study is an effort to examine the capability of a differential evolution based radial basis function neural network (RBFDE) model weekly reference evapotranspiration (ET 0 ) as climatic parameters in different agro-climatic zones (ACZs) moist sub-humid region East-Central India. ET computed using empirical equation Penman–Monteith suggested by Food and Agricultural Organization (FAO56-PM) considered target variable for investigation. performance proposed RBFDE compared with particle swarm optimization (RBFPSO), (RBFNN), multilayer artificial (MLANN) models conventional equations Hargreaves, Turc, Open-Pan, Blaney-Criddle. Weekly estimates that are obtained RBFDE, RBFPSO, RBFNN MLANN observed be more consistent than equivalent methods. For critical analysis simulation results, mean absolute percentage error (MAPE), root means square (RMSE), determination coefficient (R 2 Nash–Sutcliffe efficiency factor (NSE) computed. Low MAPE RMSE values along higher R NSE close 1, soft computing exhibit that, produce better Among models, provides improved results RBFNN, models. This method can extended estimation other ACZs.

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ژورنال

عنوان ژورنال: SN applied sciences

سال: 2021

ISSN: ['2523-3971', '2523-3963']

DOI: https://doi.org/10.1007/s42452-020-04069-z