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Evaluation of artificial neural network and Penman–Monteith equation for the prediction of barley standard evapotranspiration in a semi-arid region
- Hashemi, Masoumeh, Sepaskhah, Ali Reza
- Theoretical and applied climatology 2020 v.139 no.1-2 pp. 275-285
- barley, basins, equations, evapotranspiration, humidity, lysimeters, neural networks, prediction, semiarid zones, solar radiation, stakeholders, water allocation, water supply, water utilization, wind speed, Iran
- Evapotranspiration (ET) is a main factor of the hydrologic balance. Estimating precise ET is necessary for managing the water supply in a basin. In this study, daily barley standard evapotranspiration (DBSE) is obtained (1) directly by weighing lysimeter and (2) indirect methods. In the first step, DBSE was obtained by two weighing lysimeters in a semi-arid region (Kooshkak, Iran). In the next step, indirect methods for estimating the DBSE, the Penman–Monteith (PM), and the artificial neural networks (ANNs), including the radial basis functions (RBF), and the multi-layer perceptron (MLP), were utilized. Results showed that DBSE can be successfully calculated in semi-arid region by MLP-ANN, RBF-ANN, and PM methods. The ANN methods were offered as the best method because they need fewer input data and can be easily used for other developed programs that applied for water allocation and therefore solve the conflicts between stakeholders and optimize water usage. Finally, the sensitivity of ANNs to input data was investigated by relating changes in the daily metrological data to the dimensionless scaled sensitivities (DSS) index. Results showed that in the multi-layer perceptron, ETc was more sensitive to sunshine hours and less sensitive to wind speed and the radial basic function has different patterns which are more sensitive to sunshine hours. When the sunshine hours decrease by more than 10%, the standard crop evapotranspiration (ETc) was more sensitive to average humidity and less sensitive to wind speed.