Generalizability of Gene Expression Programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran

Soft computing approaches for forecasting reference evapotranspiration

“…Recently, explicit mathematical formulation of ET 0 can be done by using gene-expression programming (GEP) Guven, 2012, 2013;Shiri et al, 2012Shiri et al, , 2014a. Support vector machine (SVM) (Vapnik, 1995(Vapnik, , 1998 is one of the novel soft learning algorithms that has been recently realized for a wide range of applications in the field of soft computing, hydrology and environmental studies (Lee and Verri, 2003;Lu and Wang, 2005;Asefa et al, 2006;Ji and Sun, 2013;Sun, 2013).…”

Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR

“…For evaluation of the performance of the developed GEP models, the norms used in this study were correlation coefficient (R), coefficient of determination (R 2 ), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) between GEP predicted results and experimental results, as adapted in similar studies [57,[59][60][61][62].…”

Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran

“…Yassin et al (2016) compared GEP with neural networks in estimating daily ET o values under arid conditions and concluded that the neural networks −based ET o models were slightly better than GEP-based models. Also, Guven and Kisi (2011), Guven et al (2008), Shiri and Kisi (2011, 2014a, 2014b and Kisi (2016) have applied GEP for ET o modeling using different data management scenarios and climatic contexts. Genetic programming has the advantage where the structure and constants for a solution are evolved simultaneously.…”