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Comparison of M5 Model Tree and Artificial Neural Network for Estimating Potential Evapotranspiration in Semi-arid Climates | ||
| Desert | ||
| مقاله 19، دوره 19، شماره 1، فروردین 2014، صفحه 75-81 اصل مقاله (134.28 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jdesert.2014.51056 | ||
| نویسندگان | ||
| Nozar Ghahreman* ؛ Mahsa Sameti | ||
| Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran | ||
| چکیده | ||
| Evaporation is a fundamental parameter in the hydrological cycle. This study examines the performance of M5 model tree and artificial neural network (ANN) models in estimating potential evapotranspiration calculated by Penman- Monteith and Hargreaves- Samani equations. Daily weather data from two meteorological stations in a semi-arid climate of Iran, namely Kerman and Zahedan, were collected during 1995-2004 and included the mean, maximum and minimum air temperatures, dewpoint, relative humidity, sunshine hours, and wind speed. Results for both stations showed that the performance of the M5 model tree was more accurate (R=0.982 and 0.98 for Penman-Monteith equation and R=0.983 and 0.98 for Hargreaves-Samani equation in Kerman and Zahedan, respectively) than the ANN model (R=0.975 and 0.978 for Penman-Monteith equation and R=0.967 and 0.974 for Hargreaves-Samani equation in Kerman and Zahedan, respectively), but the models’ differences were insignificant at a confidence level of 95%. It also performed better at the Zahedan station using the Penman- Monteith equation. The most significant variables affecting the potential evapotranspiration in the case of the Penman–Monteith equation were found to be mean air temperature, sunshine hours, wind speed, and relative humidity. Similarly, for the Hargreaves-Samani equation, the maximum and minimum temperatures, sunshine hours, and wind speed were determined to be the most significant variables. Further studies in other climates are recommended for further analysis. | ||
| کلیدواژه‌ها | ||
| ANN؛ Machine learning؛ Penman-Monteith؛ Hargreaves-Samani | ||
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