Abstract
The amount of water allocated to irrigation systems is significantly greater than the amount allocated to other sectors. Thus, irrigation water demand management is at the center of the attention of the Ministry of Agriculture and Forestry in Turkey. To plan more effective irrigation systems in agriculture, it is necessary to accurately calculate plant water requirements. In this study, daily reference evapotranspiration (ETo) values were estimated using tree-based regression and deep learning-based gated recurrent unit (GRU) models. For this purpose, 15 input scenarios, consisting of meteorological variables including maximum and minimum temperature, wind speed, maximum and minimum relative humidity, dew point temperature, and sunshine duration, were considered. ETo values calculated according to the United Nations Food and Agriculture Organization (FAO) Penman-Monteith method were considered as model outputs. The results indicate that the random forest model, with a correlation coefficient of 0.9926, is better than the other tree-based models. In addition, the GRU model, with R = 0.9837, presents good performance relative to the other models. In this study, it was found that maximum temperature was more effective in estimating ETo than other variables.
Highlights
Today, with a growing population, reliable food production and supply are among the main policy concerns of many countries, highlighting the need to use renewable water efficiently to prevent future water shortages
There are various methods to determine plant water requirements, but the Penman-Monteith (ETo-PM) method presented by the United Nations Food and Agriculture Organization (FAO) has been accepted as the standard, since other methods give different results [2]
The results indicated that the Artificial Neural Networks (ANN)
Summary
With a growing population, reliable food production and supply are among the main policy concerns of many countries, highlighting the need to use renewable water efficiently to prevent future water shortages. There are various methods to determine plant water requirements, but the Penman-Monteith (ETo-PM) method presented by the United Nations Food and Agriculture Organization (FAO) has been accepted as the standard, since other methods give different results [2] This method calculates reference evapotranspiration values using different meteorological variables. The study showed that the estimation of ETo through the use of the M5 model tree gave better results than the ANN technique. The authors used extreme learning machine (ELM) and support vector machine (SVM) models to compare the results They used meteorological variables for 1961–2010 from weather stations in China across different climates to evaluate the models. Granata [14] estimated the actual amount of evapotranspiration using meteorological data from central Florida, USA, with a humid subtropical climate and four different machine learning methods (bagging, RF, M5P regression tree, and support vector regression) and compared the results.
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