application of meta-heuristic algorithms to estimate daily evaporation rate

Accurate estimation of daily evaporation is fundamental in the sustainable management of water resources. therefore, the purpose of this study was to investigate the application of the artificial neural network model with wavelet and firefly meta-heuristic algorithms of to estimate daily et0. to achieve this goal, two combined w-ann and fa-ann models were investigated for daily estimation of et0 in two mediterranean climates in the west of iran as a case study. daily climatic parameters including maximum and minimum temperature (t max and t min), sunshine duration (n), relative humidity (rh), wind speed (u), and evaporation et0 were collected from two weather stations from 2012-2022 and during four combined scenarios, which were used from 2012 to 2019 for model training and from 2022 to 2019 for model testing. to compare and evaluate the models, statistical indicators of the correlation coefficient, root mean square error, mean absolute value of error, normalized root mean square error, and nash sutcliffe coefficient were used. the results showed that all the investigated models performed better in combined input scenarios. the results of the evaluation criteria showed that the w-ann hybrid model has the highest daily estimation accuracy.