Multivariate modeling of pan evaporation in monthly temporal resolution using a hybrid evolutionary data-driven method (case study: Urmia Lake and Gavkhouni basins)
Alireza Emadi Sarvin Zamanzad-Ghavidel· Sina Fazeli . Soheila Zarei Ali Rashid-Niaghi
Abstract
Evaporation is an important meteorological variable that has a great impact on water resources
In the current research, climatology data, and seasonal coefficient have been used to estimate monthly pan evaporation (E pan) for 2005-2018 study years at four selected stations of the Urmia Lake basin with Dsa and six selected stations of Gavkhouni basin with Bsk climate categories, in Iran
Estimation of monthly Epan was performed using data-driven methods such as arti- ficial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and gene expression pro- griming (GEP) as well as wavelet-hybrids (WANN, WANFIS, and WGEP)
Based on the evaluation crite- ria, the WGEP model performance was better than the other models in estimating the monthly
The results indicated that WGEP and ANN are the best and poor-est models for all stations without affecting the climate condition of basins
The values of RMSE for WGEP model for stations of Urmia Lake and Gavkhouni basins were varied from 15.839 to 26.727 and 20.651 to 70.318, respectively
Also, the values of RMSE for ANN model for stations of Urmia Lake and Gavkhouni basins were varied from 29.397 to 38.452 and 30.635 to 85.237, respectively
The model’s performance was improved as a result of considering the data noise elimi- nation and applying seasonal coefficient to estimate E pan of various climatic conditions
This study with pre- senting mathematical equations for estimating monthly E pan has a significant impact on the management and pan planning of water resources policymakers in the fut
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