Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

Milan Gocić () and Mohammad Arab Amiri ()
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Milan Gocić: University of Nis

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 6, No 13, 1913-1926

Abstract: Abstract The reference evapotranspiration (ET0) 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 ET0 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 ET0 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 ET0 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 ET0 prediction in Serbia is the novelty of this study.

Keywords: Reference evapotranspiration; FAO penman-Monteith; ANN models; Optimum time lags (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02820-8

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