Comparative Evaluation of Prediction Model between Inference Fuzzy System and Universal Kriging for Spatial Data
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Abstract
This paper dealt with one of the spatial interpolation methods in the geostatistics field. The purpose of this research is to get the parameters of unbiased estimators based on regionalized random variables in spatial statistics. In this paper, we used universal kriging with the fuzzy inference system by the Mamdani technique. the objective of this work is to estimate the parameters of covariance functions relying on spatial real for the depth of groundwater in Mosul city, Iraq. The data adopted contains (100) real data with locations representing the depth. From the results we show the best model with the constructs of weights, we illustrate the performance of universal kriging is the best when corresponding with the fuzzy system. In conclusion, the improvement of any method of spatial interpolation or fuzzy system does not depend on more statistical structures but depends on the efficiency of the method which satisfies the conditions of weights and minimum variance errors. All programming is applied by Matlab language.
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References
F. Topaloğlu, & H. Pehlıvan, (2018). Comparison of Mamdani type and Sugeno type fuzzy inference systems in wind power plant installations. In 2018 6th International Symposium on Digital Forensic and Security 1–4
M. Sunjana, (2019). Prediction of Production Using the Fuzzy Mamdani Inference System. International Journal of Advanced Science and Technology (IJAST), 28(6), 136–139.
S. O. Kassim, A. G. Ali, & I. M. Harram, (2021). Design And Implementation Of Mamdani Type Fuzzy Inference System Based Water Level Controller. . IOSR Journal of Electronics and Communication Engineering, 16(4), 15–22.
G. Matheron, (n.d.). (1973). The intrinsic random functions and their applications. Advances in applied probability . . 5 ( 3 ,) 439 – 468 .
H. Wackernagel, (2003). Multivariate geostatistics: an introduction with applications. Springer Science & Business Media.
H. Xie, X. Zhang, B.Yu, & H. Sharif, (2011). Performance evaluation of interpolation methods for incorporating rain gauge measurements into NEXRAD precipitation data: a case study in the Upper Guadalupe River Basin. Hydrological Processes, 25(24), 3711–3720
A. Jain, & A. Sharma, (2020). Membership Function Formulation Methods for Fuzzy Logic Systems: a Comprehensive Review. 7(19), 2020.
M. Verma, N. K., Singh, V. Rajurkar, & Aqib. (2019). Fuzzy inference network with mamdani fuzzy inference system. In Computational Intelligence. Theories, Applications and Future Directions,” Springer, Singapore., 375–388.
L. J. Muhammad, & E. A. Algehyne, (2021). Fuzzy based expert system for diagnosis of coronary artery disease in Nigeria. Health and Technology, 11(2), 319–329.
A. A. Eldeiry, & L. A. Garcia, (2010). Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using LANDSAT images. . Irrigation and Drainage Engineering, 136(6), 355–364.
R. Giraldo, L. Herrera, and V. Leiva, (2020). Cokriging prediction using as secondary variable a functional random field with application in environmental pollution. Mathematics, 8(8), 1305.
J.P. Chiles, and P. Delfiner, (2009). Geostatistics: modeling spatial uncertainty. John Wiley & Sons.
H. A. Farag, M. A. Gouda, & M. A. Yassin, (2020). The Effect of Using Cokriging against Ordinary Kriging When Estimating Phosphate Deposits at Elsebaiya Area, Egypt. World Journal of Engineering and Technology711 ,)4(8 , .
M. Maleki, X. Emery, and N. Mery, (2017). Indicator variograms as an aid for geological interpretation and modeling of ore deposits. Minerals, 7(12), 241.
W. Somayasa, D.K. Sutiari, and W. Sutisna, (2021). Optimal prediction in isotropic spatial process under spherical type variogram model with application to corn plant data. In Journal of Physics: Conference Series, 012003.