M5 Model Trees and Neural Networks based modelling of ET0 in Ankara Turkey
Yazarlar (4)
Muhammet Taghi Sattari
Mahesh Pal Yabancı Kurumlar
Prof. Dr. Kadri YÜREKLİ Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Ali Ünlükara Erciyes Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Turkish Journal of Engineering and Environmental Sciences | ||
| Dergi ISSN | 1300-0160 Dergi Bilgileri (2013) | ||
| Dergi Tarandığı Indeksler | CSA, EBSCOhost, H.W. Wilson, Ovid, Thomson Reuters | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2013 |
| Cilt / Sayı / Sayfa | 37 / 2 / 211–219 | DOI | – |
| UAK Araştırma Alanları |
Sulama Sistemleri
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| Özet |
| This paper investigates the potential of back propagation neural network and M5 model tree based regression approaches to model monthly reference evapotranspiration using climatic data of an area around Ankara, Turkey. Input parameters include monthly total sunshine hours, air temperature, relative humidity, wind speed, rainfall, and monthly time index, whereas the reference evapotranspiration calculated by FAO{56 Penman{Monteith was used as an output for both approaches. Mean square error, correlation coe cient, and several other statistics were considered to compare the performance of both modeling approaches. The results suggest a better performance by the neural network approach with this dataset, but M5 model trees, being analogous to piecewise linear functions, provide a simple linear relation for prediction of evapotranspiration for the data ranges used in this study. Di erent scenario analysis with neural networks suggests that rainfall data does not have any in uence in predicting evapotranspiration. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Yöksis | 18 |