H. Moradi; H. Ansari; majid hashemi nia; A. Alizadeh; A. Vahidian Kamyad; S.M.J. Mosavi
Abstract
Evapotranspiration is one of the major components of hydrologic cycle and estimation of irrigation needs. In recent years the use of intelligent systems for estimating hydrological phenomena has increased significantly.In this study the possibility of using fuzzy inference system efficiency, creating ...
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Evapotranspiration is one of the major components of hydrologic cycle and estimation of irrigation needs. In recent years the use of intelligent systems for estimating hydrological phenomena has increased significantly.In this study the possibility of using fuzzy inference system efficiency, creating a bridge between meteorological parameters and evapotranspiration, and comparing the accuracy of reference evapotranspiration using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, five models for estimating daily reference evapotranspiration were presented. For these models, the calculated evapotranspirationfrom Penman-Monteith-FAO equation was considered as a baseand the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination,Jacovides(t) and Sabbaghet al. (R2/t) criteria. The used data were collected from Mashhad’s meteorological synoptic station for a period of 50-years (from 1339 to 1389).From the available data, 75 percentwas used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the fuzzy models with different input parameters as compared with Penman-Monteith-FAO and Hargreaves-Samani methods showed that fuzzy systems were very well able to estimate the daily reference evapotranspiration.Fuzzy model so that the highest correlation with the four input variables (r=0.99) had in mind and evaluate other parameters, the model with two parameters, temperature and relative humidity (RMSE=0.96, MBE =0.18, R2=0.95, t=22, = and R2 / t=0.04) match very well with the model Penman - Monteith - FAO had stage training. In the test phase, training phase was very similar results and the model with the second phase of temperature and relative humidity will get the best match. According to the results of this study it can be concluded that fuzzy model approach is an appropriate method to estimatethe daily reference evapotranspiration. In addition, the fuzzy models do not require complex calculations which are required forcombination methods.
H. Ansari; H. Moradi
Abstract
Abstract
Evapotranspiration as one of the most important components of the hydrologic cycle, plays a key role in water resources management, crop yield simulation and irrigation scheduling. Therefore, presenting a low cost and precision model is very essential for calculations of hourly ETo. Although, ...
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Abstract
Evapotranspiration as one of the most important components of the hydrologic cycle, plays a key role in water resources management, crop yield simulation and irrigation scheduling. Therefore, presenting a low cost and precision model is very essential for calculations of hourly ETo. Although, there are empirical formulas, their performances are not all satisfactory due to the complicated nature of the hourly evapotranspiration process, the data availability, and high cost and error for gathering data. This paper develops hourly ETo estimation model based on fuzzy inference system (FIS) technique. We follow the idea of using the least input parameters, so the net radiation (Rn) selected, as the only input parameter. The used data has been picked on UC-Andrade station for training model, that have the most variation on Rn and climatically conditions, and another thirteen stations, that selected randomly, among 114 automated stations in US California. There is not proper hourly data in Iran. FIS model estimates hourly ETo as crisp number using of 230 rules with 48 level, centeroid defuzzification method and inference Mamdani method. FIS results compared with Penman-Monteith-FA056 and CIMIS-Penman combined model. It has been found that FIS technique has high accuracy and good performance (for the train data set, R2 = 0.97, RMSE= 0.07, MBE=-0.004 and R2/t (t: Jacovides criteria)=0.21). Comparing FIS with CIMIS and FAO56 results shows that FIS has better correlation with CIMIS than FAO56 for test data set, with R2 = 0.94, RMSE= 0.0693, MBE=-0.0384 and R2/t=0.018. Among FIS, CIMIS and FAO56, FIS model is economical, because of the parsimony principal; in conclusion, it raises model accuracy.
Keywords: Fuzzy model, Hourly Evapotranspiration, CIMIS-Penman, Penman-Montieth-FAO56
