Modern Techniques to Modeling Reference Evapotranspiration in a Semiarid Area Based on ANN and GEP Models

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.

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“…The examined ANN models(19) and the corresponding indices of performance. The models in bold exhibit performance that is discussed in detail.…”

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confidence: 99%

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

Dimitriadou

Nikolakopoulos

2022

Water

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“…Several types of research have been conducted using ANNs to estimate evapotranspiration as a function of climatic elements (Kumar et al, 2002), (Sudheer et al, 2003), (Odhiambo et al, 2001), (Trajkovic et al, 2003), (Achite et al, 2022), (Genaidy, 2020), (Heramb et al, 2023), (Rajput et al, 2023), (Abdel-Fattah et al, 2023), (Tunalı et al, 2023), (Ekhmaj, 2012) and (Ekhmaj et al, 2013). These researches found satisfactory results, compared with those obtained from the FPM method.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Artificial Neural Networks for Estimating Refer-ence Evapotranspiration in Shahat, Libya using limited climatic data

Momen,

Abdelatty

2024

mjbs

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This study was conducted with the aim of evaluating the performance of artificial neural networks (ANNs) to estimate the reference evapotranspiration using limited climate data in Shahat region in Libya, compared to using the FAO Penman-Monteith equation (FPM), which requires temperature, wind speed, relative humidity and number of sunshine hours, which are rarely available in most meteorological stations in developing countries. In this study, we used the average temperature (Tmean) and the average relative humidity (RHmean) obtained from Shahat meteorological station for the period from 1963 to 1999, and the extraterrestrial radiation (Ra), which can be calculated given the location and time of the day. These data are divided into two groups, from 1963 to 1988 and from 1989 to 1999 for the training and validation phases of the neural networks, respectively. This study concluded that using (Tmean), (RHmean) and (Ra) gave the best agreement with the results calculated with the FAO Penman-Monteith equation, where the values of R2 and RMSE are equal to 0.98 and 0.26, respectively.

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“…Haung et al (2019); Fan et al (2018), Nourani et al (2019), Yu et al (2020), Sharafi and Ghaleni, (2021), Douna et al (2021). There have already been several conventional modeling techniques that have proven to be successful in modeling ETo using ANN Algorithm (Achite et al 2022;Gocić and Amiri, 2021;Nawandar et al 2021). Antonopoulos and Antonopoulos (2017) predicted daily ETo in northern Greece and founded that the ANN model has a more accurate prediction than other models.…”

Section: Introductionmentioning

confidence: 99%

Hybrid COOT–ANN: a novel optimization algorithm for prediction of daily reference evapotranspiration in Australia

Mirzania

Kashani

Golmohammadi

et al. 2022

Preprint

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The present study evaluates the capability of a novel optimization method in modeling daily reference evapotranspiration (ET0), a critical issue in water resource management. A hybrid predictive model based on the ANN Algorithm that is embedded within the COOT method (COOT bird natural life model- Artificial Neural Network (COOT-ANN)) is developed and evaluated for its suitability for the prediction of daily ET0 at seven meteorological stations in different states of Australia. Accordingly, a daily statistical period of 12 years (01-01-2010 to 31-12-2021) for climatic data of maximum temperature, minimum temperature, and ET0 were collected. The results are evaluated using six performance criteria metrics: correlation coefficient (R), Root Mean Square Error (RMSE), Nash-Sutcliffe efficiency (NSE), RMSE-observation standard deviation ratio (RSR), Scatter Index (SI), and mean absolute error (MAE) along with the Taylor diagrams. The performance of the COOT-ANN model was compared with those of the conventional ANN model. The results showed that the COOT-ANN hybrid model outperforms the ANN model at all seven stations; and so this study provides an innovative method for prediction in agricultural and water resources studies.

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Modern Techniques to Modeling Reference Evapotranspiration in a Semiarid Area Based on ANN and GEP Models

Cited by 12 publications

References 39 publications

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

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

Performance Evaluation of Artificial Neural Networks for Estimating Refer-ence Evapotranspiration in Shahat, Libya using limited climatic data

Hybrid COOT–ANN: a novel optimization algorithm for prediction of daily reference evapotranspiration in Australia

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