Abstract
The present study is an effort to examine the capability of a differential evolution based radial basis function neural network (RBFDE) to model weekly reference evapotranspiration (ET0) as a function of climatic parameters in different agro-climatic zones (ACZs) of a moist sub-humid region in East-Central India. The ET0 computed using the empirical equation of Penman–Monteith suggested by the Food and Agricultural Organization (FAO56-PM) is considered as a target variable for investigation. The performance of the proposed RBFDE model is compared with particle swarm optimization based radial basis function (RBFPSO), radial basis function neural network (RBFNN), multilayer artificial neural network (MLANN) models and conventional empirical equations of Hargreaves, Turc, Open-Pan, and Blaney-Criddle. Weekly ET0 estimates that are obtained using RBFDE, RBFPSO, and RBFNN and MLANN are observed to be more consistent than equivalent empirical methods. For a critical analysis of simulation results, mean absolute percentage error (MAPE), root means square error (RMSE), determination coefficient (R2) and Nash–Sutcliffe efficiency factor (NSE) is computed. Low MAPE and RMSE values along with higher R2 and NSE close to 1, obtained with soft computing models exhibit that, soft computing models produce better estimates of ET0 than empirical methods. Among the soft computing models, RBFDE provides improved results as compared to RBFPSO, RBFNN, and MLANN models. This method can be extended for ET0 estimation in other ACZs.
Highlights
In response to atmospheric demand, soil surface evaporation and transpiration from plant occurs simultaneously in a cropping field and is termed as evapotranspiration (ET) in a combined manner [1]
Input features combination of different models is decided based on empirical approaches of Hargreaves, Turc, Open Pan, Blaney-Criddle, and FAO56-PM E T0 listed in the previous section
Like the Hargreaves method, type I models include only Tmax and Tmin as input features, whereas Type II soft computing models include bright sunshine hours (BSS) with temperature, which is equivalent to the Turc approach
Summary
In response to atmospheric demand, soil surface evaporation and transpiration from plant occurs simultaneously in a cropping field and is termed as evapotranspiration (ET) in a combined manner [1]. Adamala [24] has reported improved generalized performance of wavelet neural network (WNN) and ANN model for estimation of ET0 as compared to linear regression (LR), wavelet regression (WR) and Hargreaves (HG) methods for the studied locations in different agro-ecological regions of India. Sanikhani et al [25] have applied several artificial intelligence models including multi-layer perceptron (MLP), generalized regression neural network (GRNN), integrated ANFIS systems with grid partitioning (ANFIS-GP) and subtractive clustering (AFNIS-SC), radial basis neural network (RBNN) and GEP for modeling E T0 in a cross-station scenario for different locations in Turkey and demonstrated that AI-based models performed better than the empirical equation of Hargreaves-Samani (HS) and its calibrated version (CHS) It is observed from the literature review that researchers have successfully implemented various types of hybrid soft computing models combining conventional neural networks along with evolutionary computing algorithms for estimation of ET0. The square depicts the simulated mean, and the straight-line shows the observed mean
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