Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece

Abstract

The aim of the study was to investigate the utility of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) on the Peloponnese Peninsula in Greece for two representative months of wintertime and summertime during 2016–2019 and to test if using fewer inputs could lead to satisfactory predictions. Datasets from sixty-two meteorological stations were employed. The available inputs were mean temperature (Tmean), sunshine (N), solar radiation (Rs), net radiation (Rn), vapour pressure deficit (es-ea), wind speed (u2) and altitude (Z). Nineteen Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) models were tested and compared against the corresponding FAO-56 Penman Monteith (FAO PM) estimates of a previous study, via statistical indices. The MLP1 7-2 model with all the variables as inputs outperformed the rest of the models (RMSE = 0.290 mm d−1, R2 = 98%). The results indicate that even ANNs with simple architecture can be very good predictive models of ETo for the Peloponnese, based on the literature standards. The MLP1 model determined Tmean, followed by u2, as the two most influential factors for ETo. Moreover, when one input was used (Tmean, Rn), RBFs slightly outperformed MLPs (RMSE < 0.385 mm d−1, R2 ≥ 96%), which means that even a sole-input ANN resulted in satisfactory predictions of ETo.

Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data

“…In contrast, the full inputs involving Tmin, Tmax, Ra, and MN generally perform the best. The studies by Dimitriadou and Nikolakopoulos [39,40] also found similar results. They applied multiple linear regression and ANN for predicting ET0 of the Peloponnese Peninsula, Greece.…”

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

“…It was found that, mostly in August, (wind speed is generally very low in summer), in cases where increased u 2 values occurred, ETo was directly affected. This deduction is confirmed by the latest study on ETo across the Peloponnese, in which u 2 was proven to be the most influential parameter after Tmean [73]. In conclusion, it is probable that the relationship between u 2 and ETo is non-linear, thus the MLR model would not depict the established relationship.…”

“…In other words, MLR aims to find the linear function that minimizes the sum of the squares of errors (SSE) between the observed and the predicted data. An advantage of this method is the easy interpretation of the coefficients, which are generated in the model with low computational effort, in comparison to more complex techniques, such as energy balance methods and artificial intelligence algorithms [13][14][15][16][17][18][19][20][21][24][25][26][27][28][29][30][37][38][39][40][41][42][43][67][68][69][70][71][72][73][74][75]. For the MLR model, the response (dependent) variable y is assumed to be a function of k independent variables x i .…”

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

“…Dimitriadou and Nikolakopoulos [31] evaluate the performance of the Artificial Neural Network (ANN) for the Peloponnese Peninsula in Greece. The dataset comprising of mean temperature (Tmean), sunshine (N), solar radiation (Rs), net radiation (Rn), vapor pressure deficit (es-ea), wind speed (u2), and altitude (Z) from the year 2016 to 2019 for sixty-two weather stations.…”