Abstract
In this study, a hybrid algorithm of adaptive neuro fuzzy inference system (ANFIS), particle swarm optimization (PSO) and principle component analysis (PCA) is utilized to predict the reference evapotranspiration (ET0). The accuracy of the computational model is evaluated using four statistical tests including Pearson correlation coefficient (r), mean square error (MSE), root mean-square error (RMSE), and coefficient of determination (R2). The results show that the ET0 can be estimated with an acceptable accuracy trough combination of PCA and ANFIS. Moreover, the result indicated that the ANFIS model can be simplified via reducing dimensionality of the input data.
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Acknowledgment
Dr. Mosavi carried out this research during the tenure of an ERCIM Alain Bensoussan Fellowship Programme. Furthermore, the support and research infrastructure of Institute of Advanced Studies Koszeg, iASK, is acknowledged.
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Mosavi, A., Edalatifar, M. (2019). A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration. In: Laukaitis, G. (eds) Recent Advances in Technology Research and Education. INTER-ACADEMIA 2018. Lecture Notes in Networks and Systems, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-319-99834-3_31
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