Modeling Of Monthly Evaporation Using Single and Hybrid-Wavelet Data-Driven Methods in Basins of Iran with Climate Variety

Evaporation as one of the natural parameters has always been considered by researchers. In this study, the monthly evaporation variable was modeled in two different climates of Iran using artificial neural network, adaptive fuzzy-neural inference system and gene expression programming methods and combining these methods with wavelet theory. For this purpose, meteorological data of precipitation, relative humidity, average temperature, maximum temperature, minimum temperature and wind speed were used during the statistical period of 1384-1397 related to the two catchments of Urmia Lake and Gavkhouni. In this study, the seasonal effect and data noise reduction were applied. The accuracy of the studied methods was evaluated based on statistical correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and nash-sutcliffe efficiency (NSE). The results show that in two different climates, the wavelet-hybrid gene expression programming and the single artificial neural network have the highest and weakest performance, respectively, among other data mining models used in this study. The hybrid wavelet-gene expression programming model with RMSE value of 20.870 and 156.884 had higher performance for Tazehkand station in Urmia Lake catchment area and Kuhpayeh catchment in Gavkhouni catchment area, respectively. Also, the results showed that the effect of seasonal factor utilization and data noise reduction in model performance improvement is significant. Based on the results of the models performance Urmia Lake catchment area with Dsa climate has been better. However, hybrid data mining methods can be introduced as a good alternative to the old methods.