Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation

Accurately modeling pan evaporation is important in water resources planning and management and also in environmental engineering. This study compares the accuracy of two new data-driven methods, multi-gene genetic programming (MGGP) approach and dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. The climatic data, namely, minimum temperature, maximum temperature, solar radiation, relative humidity, wind speed, and pan evaporation, obtained from Antakya and Antalya stations, Mediterranean Region of Turkey were utilized in the study. The MGGP and DENFIS methods were also compared with genetic programming (GP) and calibrated version of Hargreaves Samani (CHS) empirical method. For Antakya station, GP had slightly better accuracy than the MGGP and DENFIS models and all the data-driven models performed were superior to the CHS while the DENFIS provided better performance than the other models in modeling pan evaporation at Antalya station. The effect of periodicity input to the models' accuracy was also investigated and it was found that adding periodicity significantly increased the accuracy of MGGP and DENFIS models.