ANN-Based Model to Predict Reference Evapotranspiration for Irrigation Estimation

Nawandar, Neha K.; Cheggoju, Naveen; Satpute, V. R.

doi:10.1007/978-981-15-7234-0_63

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…The inputs included climate and non-climate (such as altitude) variables that affect ETo [83], namely, solar radiation (Rs), net radiation (Rn), sunshine hours (N), mean air temperature (Tmean), vapour pressure deficit (es-ea), wind speed at 2 m from the surface (u 2 ) and altitude (Z). The first three were previously calculated as functions of the station latitude and the Julian day, as in Zanetti et al (2008) [36]. Based on Rahimikhoob (2020) [84], the input combinations were subsequently limited, aiming to explore the possibility of producing acceptably accurate predictions of ETo with fewer variables than the available seven or even with a sole input variable (such as Tmean or Rn).…”

Section: Methodsmentioning

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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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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Section: Methodsmentioning

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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“…Прогноз ETo (эталонной эвапотранспирации) с использованием погодной переменной для определения графика орошения [27] Prediction of ETo (reference evapotranspiration) using weather variable to decide on irrigation scheduling [ 27] ✓ × ✓…”

Section: Annunclassified

Implementation of Artificial Intelligence in Agriculture to Optimize Irrigation

Fedosov

Menshikh

2022

S-h. maš. tehnol.

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Abstract. The relevance of artificial intelligence in agriculture is substantiated for irrigation optimization. (Research purpose) To report on the progress made over the past few years in the application of artificial intelligence to optimize crop irrigation. (Materials and methods) The review focuses on the most salient facts and important scientific information on the application of artificial intelligence in crop production. The review is based on Various databases (Google Scholar, PubMed, Science Direct, SciFinder, Web of Science, RSCI) and online sources (Research Gate, Springer Nature Open Access, Wiley Online Library). It is shown how the integration of machine learning models can provide intelligent irrigation management. The review reports on the research trends and applicability of machine learning methods, as well as the deployment of developed machine learning models for sustainable irrigation management. (Results and discussion) Mobile and web platforms are shown to be able to facilitate intelligent irrigation management. Machine learning proves to be one of the central areas of artificial intelligence helping researchers to work more creatively and efficiently. The review notes the problems of introducing artificial intelligence in crop production and specifies the future research areas in the machine learning implementation and digital farming solutions. (Conclusions) The relevance of the intelligent system in irrigation and water management is proved for sustainable agriculture. It is revealed that, despite the extensive literature available, machine learning modeling for crop irrigation management is still in its infancy. The countries leading in this area are China, the United States and Australia.

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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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ANN-Based Model to Predict Reference Evapotranspiration for Irrigation Estimation

Cited by 7 publications

References 13 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

Implementation of Artificial Intelligence in Agriculture to Optimize Irrigation

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

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