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Full-Text Articles in Other Civil and Environmental Engineering
Estimation Of Reference Crop Evapotranspiration Using Fuzzy State Models, Lameck O. Odhiambo, R. E. Yoder, D. C. Yoder
Estimation Of Reference Crop Evapotranspiration Using Fuzzy State Models, Lameck O. Odhiambo, R. E. Yoder, D. C. Yoder
Biological Systems Engineering: Papers and Publications
Daily evapotranspiration (ET) rates are needed for irrigation scheduling. Owing to the difficulty of obtaining accurate field measurements, ET rates are commonly estimated from weather parameters. A few empirical or semi–empirical methods have been developed for assessing daily reference crop ET, which is converted to actual crop ET using crop coefficients. The FAO Penman–Monteith method, which is now accepted as the standard method for the computation of daily reference ET, is sophisticated. It requires several input parameters, some of which have no actual measurements but are estimated from measured weather parameters. In this study, we examined the suitability of fuzzy …
Optimization Of Fuzzy Evapotranspiration Model Through Neural Training With Input–Output Examples, Lameck O. Odhiambo, R. E. Yoder, D. C. Yoder, J. W. Hines
Optimization Of Fuzzy Evapotranspiration Model Through Neural Training With Input–Output Examples, Lameck O. Odhiambo, R. E. Yoder, D. C. Yoder, J. W. Hines
Biological Systems Engineering: Papers and Publications
In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman–Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial–and–error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy–neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on …