Evaluation of a neuro-fuzzy technique in estimating pan evaporation values in low-altitude locations

The evaporation of water from free-water surfaces or land surfaces is one of the main components of the hydrological cycle and is a complex outcome of various meteorological and physical parameters. A simulation procedure was evaluated in this paper to study the capabilities of a neuro-fuzzy (NF) technique to estimate daily pan evaporation (E-P) magnitudes using meteorological variables. The assessment of the NF technique for simulating E-P values was performed via local (temporal) and external (spatial) data-management scenarios. Hence, a thoroughgoing scan of the possible train and test-set combinations was carried out based on the temporal and spatial criteria using k-fold testing procedures. A comparison was also made between the NF and neural networks (NN) methods using the same data and procedures. The obtained outcomes revealed that the proposed generalized NF and NN models have good abilities at simulating E-P values using meteorological data. Therefore, the calibration of local NF models might not be required if sufficient meteorological parameters exist in other weather stations. The results also demonstrated that a proper assessment of the models' performance accuracy should include a complete temporal/spatial scanning of the used patterns.