Abstract:Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, ε-support vector regression (ε-SVR) and ν-support vector regression (ν-SVR) were, respectively, used to construct quantitative structure-property relationship (QSPR) models of Q and e parameters in the Q-e scheme, which is remarkably useful in the interpretation of the reactivity of a monomer in free-radical copolymerizations. The quantum chemical descriptors used to d… Show more
“…Closer proximity to the core of the protected area affords a much higher level of protection as compared to buffer zones; core areas are thus subject to lower rates of forest loss and resource extraction. Another major cause of AGB loss that is apparent not only in Angkor Thom, but also in other parts of tropical Asia [32] is the proximity of an area to various sources of anthropogenic disturbance, such as roads.…”
Section: Spatial Patterns Of Agb In Angkor Thommentioning
confidence: 99%
“…The value of ϕ(x) is not known, and kernel functions are used for mapping the predictor variables into higher dimensions [32]. Radial basis function (RBF) is a kernel function that is commonly used to account for such data and which produces robust results compared to other kernels [33,34].…”
This study develops a modelling framework for utilizing very high-resolution (VHR) aerial imagery for monitoring stocks of above-ground biomass (AGB) in a tropical forest in Southeast Asia. Three different texture-based methods (grey level co-occurrence metric (GLCM), Gabor wavelets and Fourier-based textural ordination (FOTO)) were used in conjunction with two different machine learning (ML)-based regression techniques (support vector regression (SVR) and random forest (RF) regression). These methods were implemented on both 50-cm resolution Digital Globe data extracted from Google Earth™ (GE) and 8-cm commercially obtained VHR imagery. This study further examines the role of forest biophysical parameters, such as ground-measured canopy cover and vertical canopy height, in explaining AGB distribution. Three models were developed using: (i) horizontal canopy variables (i.e., canopy cover and texture variables) plus vertical canopy height; (ii) horizontal variables only; and (iii) texture variables only. AGB was variable across the site, ranging from 51.02 Mg/ha to 356.34 Mg/ha. GE-based AGB estimates were comparable to OPEN ACCESS Remote Sens. 2015, 7 5058 those derived from commercial aerial imagery. The findings demonstrate that novel use of this array of texture-based techniques with GE imagery can help promote the wider use of freely available imagery for low-cost, fine-resolution monitoring of forests parameters at the landscape scale.
“…Closer proximity to the core of the protected area affords a much higher level of protection as compared to buffer zones; core areas are thus subject to lower rates of forest loss and resource extraction. Another major cause of AGB loss that is apparent not only in Angkor Thom, but also in other parts of tropical Asia [32] is the proximity of an area to various sources of anthropogenic disturbance, such as roads.…”
Section: Spatial Patterns Of Agb In Angkor Thommentioning
confidence: 99%
“…The value of ϕ(x) is not known, and kernel functions are used for mapping the predictor variables into higher dimensions [32]. Radial basis function (RBF) is a kernel function that is commonly used to account for such data and which produces robust results compared to other kernels [33,34].…”
This study develops a modelling framework for utilizing very high-resolution (VHR) aerial imagery for monitoring stocks of above-ground biomass (AGB) in a tropical forest in Southeast Asia. Three different texture-based methods (grey level co-occurrence metric (GLCM), Gabor wavelets and Fourier-based textural ordination (FOTO)) were used in conjunction with two different machine learning (ML)-based regression techniques (support vector regression (SVR) and random forest (RF) regression). These methods were implemented on both 50-cm resolution Digital Globe data extracted from Google Earth™ (GE) and 8-cm commercially obtained VHR imagery. This study further examines the role of forest biophysical parameters, such as ground-measured canopy cover and vertical canopy height, in explaining AGB distribution. Three models were developed using: (i) horizontal canopy variables (i.e., canopy cover and texture variables) plus vertical canopy height; (ii) horizontal variables only; and (iii) texture variables only. AGB was variable across the site, ranging from 51.02 Mg/ha to 356.34 Mg/ha. GE-based AGB estimates were comparable to OPEN ACCESS Remote Sens. 2015, 7 5058 those derived from commercial aerial imagery. The findings demonstrate that novel use of this array of texture-based techniques with GE imagery can help promote the wider use of freely available imagery for low-cost, fine-resolution monitoring of forests parameters at the landscape scale.
The empirical parameters of copolymerization Q-e have been examined as an endpoint for establishing the quantitative structure -property relationships (QSPRs). The possibility to build up QSPR for these parameters is demonstrated for 22 transfer chain agents. Data for 20 taken in the literature and two were investigated in direct experiment. The statistical qualities of the models for parameter e together with the negative decimal logarithm of Q×10 −4 (pQ) are quite good. The mechanistic interpretation for these models are suggested and discussed.
“…APT charge on an atom is related to trace of the corresponding tensor of derivatives of dipole moment with respect to Cartesian coordinates of that atom [17] . Support vector machine (SVM) is a set of learning algorithm mainly used to resolve the classification and regression problem [8,9,[18][19][20][21][22][23] . In SVM, systems use the input data into a high dimensional feature space and subsequently carry out the linear regression in the feature space.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore Equation 1 is extremely useful in predicting and controlling the composition of any copolymer produced from any pair of monomers at any concentration ratios [1,2] . But Equation 1 may be limited because of the shortage of the values of r 12 and r 21 . The Q-e scheme can be used to estimate the monomer reactivity ratios with following equations [1][2][3] [ ] …”
Abstract:In comparison with the Q-e scheme, the Revised Patterns Scheme: the U, V Version (the U-V scheme) has greatly improved both its accessibility and its accuracy in interpreting and predicting the reactivity of a monomer in free-radical copolymerizations. Quantitative structure-activity relationship (QSAR) models were developed to predict the reactivity parameters u and v of the U-V scheme, by applying genetic algorithm (GA) and support vector machine (SVM) techniques. Quantum chemical descriptors used for QSAR models were calculated from transition state species with structures C
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