2021
DOI: 10.13189/ms.2021.090605
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Trigonometric Ratios Using Algebraic Methods

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“…For example, a two-dimensional curve characterized by the relationship between the methane concentration and distance S at different times, as well as the methane concentration change over time at different positions, is insufficiently representative (see Figure 2 in [26]) owing to the deformation of a three-dimensional surface when it is projected onto a two-dimensional plane. This conceptual imperfection in the methodological approach does not allow for adequately Spatial analysis of heterogeneous data remains one of the most difficult tasks of predicting the distribution of the response function over the factor space [52], solved using different approaches: fuzzy logic in MATLAB fuzzy logic [53]; stochastic modeling [54][55][56]; wavelet analysis with the Morlet algorithm (CWT) [57,58]; analytical methods based on using trigonometric relationships for quadratic surfaces [59]; nearest neighbor method [60]; inverse weighted distance (IDW) method [61]; multivariate nonlinear regression in SPSS software, www.ibm.com/spss [62]; and machine learning [63], fuzzy cognitive map (FCM) [64], or artificial neural networks (ANN) [65], using GIS technologies-crunching methods [66][67][68]. At the same time, deterministic methods of three-dimensional data interpolation have not lost their relevance [69].…”
Section: Methodsmentioning
confidence: 99%
“…For example, a two-dimensional curve characterized by the relationship between the methane concentration and distance S at different times, as well as the methane concentration change over time at different positions, is insufficiently representative (see Figure 2 in [26]) owing to the deformation of a three-dimensional surface when it is projected onto a two-dimensional plane. This conceptual imperfection in the methodological approach does not allow for adequately Spatial analysis of heterogeneous data remains one of the most difficult tasks of predicting the distribution of the response function over the factor space [52], solved using different approaches: fuzzy logic in MATLAB fuzzy logic [53]; stochastic modeling [54][55][56]; wavelet analysis with the Morlet algorithm (CWT) [57,58]; analytical methods based on using trigonometric relationships for quadratic surfaces [59]; nearest neighbor method [60]; inverse weighted distance (IDW) method [61]; multivariate nonlinear regression in SPSS software, www.ibm.com/spss [62]; and machine learning [63], fuzzy cognitive map (FCM) [64], or artificial neural networks (ANN) [65], using GIS technologies-crunching methods [66][67][68]. At the same time, deterministic methods of three-dimensional data interpolation have not lost their relevance [69].…”
Section: Methodsmentioning
confidence: 99%