Evaluating fuzzy inference systems for estimating hourly reference evapotranspiration (Case Study: Fariman)

The most important component of the hydrologic cycle, which plays a key role in water resource management, crop yield simulation, and irrigation scheduling is evapotranspiration. Therefore, developing a low cost and precise model is very essential for hourly ETo calculations. Although, there are numerous empirical formulas, due to the complicated nature of the hourly evapotranspiration event, the data availability, high cost, and data gathering error, their performances are not all satisfactory. Thereafter, this paper develops an hourly ETo estimation model based on fuzzy inference system (FIS) technique. After analyzing the different models and different combinations of hourly meteorological data, hourly reference evapotranspiration calculated with four fuzzy models. Penman-Montieth-FAO56 Model considered as the comparison basis for hourly estimating reference evapotranspiration models. Comparing models was done with mean root squared error, mean deviation error, coefficient of determination, Jacovides (t) and Sabagh, et al (R2 / t) criteria. The Required data gathered from the private weather station in Fariman city. With removing missing data, 9128 hourly data extracted from two-year statistical period, 2008-2009. Meanwhile, 70 percent of the data was used for model training, and 30 percent for model testing. The results showed that, fuzzy model output is acceptable in relation to Penman-Montieth-FAO56 and ASCE models output. The fuzzy model with four inputs has the highest correlation (0.99) to reference model. The fuzzy model with two inputs: solar radiation and relative humidity, presented proper values for evaluation criteria (RMSE=0.048, MBE=-0.018, R2=0.97, t=32, and R2/t=0.0295) in training phase. Under the testing phase, results were very similar to training phase. The comparison of Fuzzy model outputs with ASCE models also indicated that fuzzy model with three inputs of radiation, relative humidity, and temperature has the highest matching value (RMSE=0.05, MBE=-0.014, R2=0.95, t=13.9 and R2/t=0.068), in the training phase, which was justified with testing results. According to this study, fuzzy model can be a proper method for estimating hourly reference evapotranspiration. While, fuzzy model is simple, accurate, and does not have complex calculations like hybrid models.