Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

• Published in: Water resources management Vol. 35; no. 6; pp. 1913 - 1926
• Main Authors: Gocić, Milan, Arab Amiri, Mohammad
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-04-2021; Springer Nature B.V
• Subjects: Agricultural management; Air temperature; Artificial intelligence; Artificial neural networks; Atmospheric Sciences; Civil Engineering; Correlation; Datasets; Earth and Environmental Science; Earth Sciences; Empirical equations; Environment; Evapotranspiration; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology/Water Resources; Irrigation water; Learning algorithms; Learning theory; Machine learning; Meteorological data; Model accuracy; Monthly; Neural networks; Predictions; Resource management; Statistical methods; Structure-function relationships; Time series; Vapor pressure; Vapour pressure; Water management; Water resources; Weather stations; Wind speed; Serbia; Reference evapotranspiration; FAO penman-Monteith; ANN models; Optimum time lags
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-021-02820-8

The reference evapotranspiration (ET
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) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET
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using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET
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time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET
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prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET
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prediction in Serbia is the novelty of this study.