scholarly journals Global Tree Taper Modelling: A Review of Applications, Methods, Functions, and Their Parameters

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 913
Author(s):  
Serajis Salekin ◽  
Cristian Higuera Catalán ◽  
Daniel Boczniewicz ◽  
Darius Phiri ◽  
Justin Morgenroth ◽  
...  
Keyword(s):  
Measurement Techniques ◽  
Principal Component ◽  
Climatic Conditions ◽  
Modern Times ◽  
Taper Function ◽  
Complex Functions ◽  
Artificial Neural Network Ann ◽  
Species Specific ◽  
Simple Equations ◽  
Taper Equations

Taper functions are important tools for forest description, modelling, assessment, and management. A large number of studies have been conducted to develop and improve taper functions; however, few review studies have been dedicated to addressing their development and parameters. This review summarises the development of taper functions by considering their parameterisation, geographic and species-specific limitations, and applications. This study showed that there has been an increase in the number of studies of taper function and contemporary methods have been developed for the establishment of these functions. The reviewed studies also show that taper functions have been developed from simple equations in the early 1900s to complex functions in modern times. Early taper functions included polynomial, sigmoid, principal component analysis (PCA), and linear mixed functions, while contemporary machine learning (ML) approaches include artificial neural network (ANN) and random forest (RF). Further analysis of the published literature also shows that most of the studies of taper functions have been carried out in Europe and the Americas, meaning most taper equations are not specifically applicable to tropical tree species. Developing well-conditioned taper functions requires reducing the variation due to species, measurement techniques, and climatic conditions, among other factors. The information presented in this study is important for understanding and developing taper functions. Future studies can focus on developing better taper functions by incorporating emerging remote sensing and geospatial datasets, and using contemporary statistical approaches such as ANN and RF.

scholarly journals Radial Growth Response of European Larch Provenances to Interannual Climate Variation in Poland

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 334
Author(s):  
Norbert Szymański ◽  
Sławomir Wilczyński
Keyword(s):  
Radial Growth ◽  
Climatic Factors ◽  
Principal Component ◽  
Climatic Conditions ◽  
Growth Responses ◽  
European Larch ◽  
Temperature And Precipitation ◽  
Provenance Trials ◽  
High Precipitation ◽  
Climatic Elements

The present study identified the similarities and differences in the radial growth responses of 20 provenances of 51-year-old European larch (Larix decidua Mill.) trees from Poland to the climatic conditions at three provenance trials situated in the Polish lowlands (Siemianice), uplands (Bliżyn) and mountains (Krynica). A chronology of radial growth indices was developed for each of 60 European larch populations, which highlighted the interannual variations in the climate-mediated radial growth of their trees. With the aid of principal component, correlation and multiple regression analysis, supra-regional climatic elements were identified to which all the larch provenances reacted similarly at all three provenance trials. They increased the radial growth in years with a short, warm and precipitation-rich winter; a cool and humid summer and when high precipitation in late autumn of the previous year was noted. Moreover, other climatic elements were identified to which two groups of the larch provenances reacted differently at each provenance trial. In the lowland climate, the provenances reacted differently to temperature in November to December of the previous year and July and to precipitation in September. In the upland climate, the provenances differed in growth sensitivity to precipitation in October of the previous year and June–September. In the mountain climate, the provenances responded differently to temperature and precipitation in September of the previous year and to precipitation in February, June and September of the year of tree ring formation. The results imply that both climatic factors and origin (genotype), i.e., the genetic factor, mediate the climate–growth relationships of larch provenances.

Thermodynamics Energy for Both Supervised and Unsupervised Learning Neural Nets at a Constant Temperature

1999 ◽  
Vol 09 (03) ◽  
pp. 175-186 ◽  
Author(s):  
HAROLD SZU
Keyword(s):  
Free Energy ◽  
Constant Temperature ◽  
Helmholtz Free Energy ◽  
Principal Component ◽  
Neural Nets ◽  
Mixing Condition ◽  
Smart Cameras ◽  
Minimization Principle ◽  
Output Entropy ◽  
Artificial Neural Network Ann

Unified Lyaponov function is given for the first time to prove the learning methodologies convergence of artificial neural network (ANN), both supervised and unsupervised, from the viewpoint of the minimization of the Helmholtz free energy at the constant temperature. Early in 1982, Hopfield has proven the supervised learning by the energy minimization principle. Recently in 1996, Bell & Sejnowski has algorithmically demonstrated. Independent Component Analyses (ICA) generalizing the Principal Component Analyses (PCA) that the continuing reduction of early vision redundancy happens towards the "sparse edge maps" by maximization of the ANN output entropy. We explore the combination of both as Lyaponov function of which the proven convergence gives both learning methodologies. The unification is possible because of the thermodynamics Helmholtz free energy at a constant temperature. The blind de-mixing condition for more than two objects using two sensor measurement. We design two smart cameras with short term working memory to do better image de-mixing of more than two objects. We consider channel communication application that we can efficiently mix four images using matrices [AO] and [Al] to send through two channels.

scholarly journals FAME (v1.0): a simple module to simulate the effect of planktonic foraminifer species-specific habitat on their oxygen isotopic content

2018 ◽  
Vol 11 (9) ◽  
pp. 3587-3603 ◽  
Author(s):  
Didier M. Roche ◽  
Claire Waelbroeck ◽  
Brett Metcalfe ◽  
Thibaut Caley
Keyword(s):  
Late Holocene ◽  
Simple Module ◽  
Climatic Conditions ◽  
Model Data ◽  
Planktonic Foraminifer ◽  
Planktonic Foraminifers ◽  
Oxygen Isotopic ◽  
Species Specific ◽  
Almost All ◽  
Multiproxy Approach

Abstract. The oxygen-18 to oxygen-16 ratio recorded in fossil planktonic foraminifer shells has been used for over 50 years in many geoscience applications. However, different planktonic foraminifer species generally yield distinct signals, as a consequence of their specific living habitats in the water column and along the year. This complexity is usually not taken into account in model–data integration studies. To overcome this shortcoming, we developed the Foraminifers As Modeled Entities (FAME) module. The module predicts the presence or absence of commonly used planktonic foraminifers and their oxygen-18 values. It is only forced by hydrographic data and uses a very limited number of parameters, almost all derived from culture experiments. FAME performance is evaluated using the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) Late Holocene planktonic foraminifer calcite oxygen-18 and abundance datasets. The application of FAME to a simple cooling scenario demonstrates its utility to predict changes in planktonic foraminifer oxygen-18 to oxygen-16 ratio in response to changing climatic conditions.

scholarly journals Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
David Cárdenas-Peña ◽  
Diego Collazos-Huertas ◽  
German Castellanos-Dominguez
Keyword(s):  
Neural Network ◽  
Characteristic Curve ◽  
Principal Component ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Dementia Diagnosis ◽  
Target Matrix ◽  
Computer Aided ◽  
Artificial Neural Network Ann ◽  
Aided Diagnosis

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014CADDementiachallenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing.

scholarly journals COMPATIBILITY BETWEEN THE STEM VOLUME AND TAPER EQUATIONS VOLUME FOR BLACK WATTLE TREES

FLORESTA ◽  
2020 ◽  
Vol 50 (3) ◽  
pp. 1518
Author(s):  
Marcos Behling ◽  
Henrique Soares Koehler ◽  
Alexandre Behling
Keyword(s):  
Traditional Approach ◽  
Stem Volume ◽  
System Of Equations ◽  
Data Set ◽  
Taper Function ◽  
Black Wattle ◽  
Taper Equations ◽  
Volume Estimates ◽  
Forest Engineering

A system of equations widely used in Forest Engineering by the international community of researchers consists of a combination of a volumetric function and a taper function, with the purpose of making volume estimates compatible. When using the volume function and the taper function in a system, the result of the volume estimated by the two functions should be compatible, meaning that the volume estimated by the volumetric function should not differ from the volume obtained by integrating the taper function. Thus, the purpose of this paper was to develop and present the procedures of a system of equations to make volume estimates from both volume and taper equations compatible, and then compare it to the traditional approach, which is used in forestry companies. The procedures proposed were applied to a data set on the Acacia mearnsii De Wild. (black wattle) at sites where the plantation of this species is concentrated in the state of Rio Grande do Sul. The data set included 343 trees ranging from 5 to 10.75 years of age. It was noted that the lack of volume compatibility, in absolute terms, grows exponentially with the size of the tree. The quality of the estimates using the system of compatible equations did not differ from those obtained from the traditional model, therefore, the former is preferable. Furthermore, it was noted that the residuals from the volume and taper equations are correlated, which suggests that the system of equations be fitted simultaneously.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression

2013 ◽  
pp. 273-289
Author(s):  
Jiansheng Wu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Principal Component ◽  
Linear Regression Models ◽  
Rainfall Forecasting ◽  
Forecasting Accuracy ◽  
Parametric Regression ◽  
Nonlinear Regression Models ◽  
Linear And Nonlinear ◽  
Artificial Neural Network Ann

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

scholarly journals Process of forming a regional recreation system in the conditions of a border region

2020 ◽  
Vol 164 ◽  
pp. 07028
Author(s):  
Anastasia Vasilieva ◽  
Raisa Belaya
Keyword(s):  
Regional Policy ◽  
Principal Component ◽  
Environmental Safety ◽  
The United States ◽  
Climatic Conditions ◽  
Border Region ◽  
Significant Heterogeneity ◽  
Factor Rotation ◽  
The Face ◽  
Definition Of

Significant heterogeneity of the level of development of the Russian border, including in the field of recreation, imposes requirements for differentiation in the regional policy. Definition of the types of territories helps to solve applied management tasks more effectively. In this context, the factors by which these types were formed are important. To solve this problem, the authors conducted a factor analysis through the principal component method using oblique factor rotation. Three blocks of variables were analyzed that characterize the subjects of the Russian Federation that have land borders on the mainland (including river and lake borders) and sea borders with neighboring countries located on the map clockwise from Norway to the United States (border regions of Russia) for the period from 2010 to 2018. As a result, five factors were identified: the factor of the demand for the services of the recreational system, the factor of the development of the infrastructure of the recreational system in climatic conditions, the environmental safety factor, the factor of investment in the development of the recreational system infrastructure, the factor of the location at the border. The results of the study can be used as a practical tool for developing recommendations in the field of regional policy aimed at development of a recreational system, taking into account the factors determined for each identified group. The results of the study were obtained in the framework of the state task of the IE KarRC RAS on the topic “Institutions and social inequality in the face of global challenges and regional restrictions”.

scholarly journals Epiphytic cyanobacterial diversity in the sub-Himalayan belt of Garhwal region of Uttarakhand, India

2012 ◽  
Vol 8 ◽  
pp. 77-89 ◽  
Author(s):  
Mukesh Kumar ◽  
Rajan Kumar Gupta ◽  
AB Bhatt ◽  
SC Tiwari
Keyword(s):  
Significant Variation ◽  
Principal Component ◽  
Climatic Conditions ◽  
Oxygenic Photosynthesis ◽  
Plant Science ◽  
Himalayan Belt ◽  
Different Seasons ◽  
Cyanobacterial Diversity ◽  
Species Study ◽  
Properties Of Soil

Cyanobacteria constitute the largest, most diverse and widely distributed group of prokaryotes that perform oxygenic photosynthesis. These are known to comprise a diverse flora of morphologically distinct forms. Some species are epiphytic occurring on a variety of plants. The present study was undertaken to study the distribution pattern of epiphytic cyanobacterial flora in the foot-hills of Garhwal Himalaya. An extensive survey was carried out in different seasons at four cyanobacteria-rich localities (Dakpatthar, Kotdwar, Rishikesh and Laldhang) of Uttarakhand state of India. A total of 39 epiphytic cyanobacterial taxa (12 heterocystous and 27 non-heterocystous) belonging to 2 orders, 7 families and 17 genera were recorded from this region. Highest number of species (25) was reported from Rishikesh, followed by Kotdwar with 14 species and Laldhang and Dakpatthar each with 12 species. Principal Component Analysis showed significant variation for epiphytic cyanobacterial diversity among studied sites, whereas cluster analysis categorized epiphytic cyanobacterial diversity under two categories, viz. Cluster I with 9 species and Cluster II with 30 species. Study concludes that variation in epiphytic cyanobacterial diversity might be compared to physicochemical properties of soil and climatic conditions along altitudes.doi: http://dx.doi.org/10.3126/botor.v8i0.5955 Botanica Orientalis – Journal of Plant Science (2011) 8: 77-89

scholarly journals Performance Analysis and Soil Quality Indexing for Dalbergia sissoo Roxb. Grown in Marginal and Degraded Land of Eastern Uttar Pradesh, India

Land ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 63 ◽  
Author(s):  
Sheikh Adil Edrisi ◽  
Vishal Tripathi ◽  
Purushothaman Chirakkuzhyil Abhilash
Keyword(s):  
Soil Quality ◽  
Microbial Biomass Carbon ◽  
Uttar Pradesh ◽  
Principal Component ◽  
Climatic Conditions ◽  
Dalbergia Sissoo ◽  
Land Restoration ◽  
Degraded Land ◽  
Soil Quality Index ◽  
Degraded Lands

The successful utilization of marginal and degraded lands for biomass and bioenergy production depends upon various factors such as climatic conditions, the adaptive traits of the tree species and their growth rate and respective belowground responses. The present study was undertaken to evaluate the growth performance of a bioenergy tree (Dalbergia sissoo Roxb.) grown in marginal and degraded land of the Mirzapur district of Uttar Pradesh, India and to analyze the effect of D. sissoo plantations on soil quality improvement over the study years. For this, a soil quality index (SQI) was developed based on principal component analysis (PCA) to understand the effect of D. sissoo plantations on belowground responses. PCA results showed that among the studied soil variables, bulk density (BD), moisture content (MC), microbial biomass carbon (MBC) and soil urease activity (SUA) are the key variables critically influencing the growth of D. sissoo. The SQI was found in an increasing order with the growth period of D. sissoo. (i.e., from 0.419 during the first year to 0.579 in the fourth year). A strong correlation was also observed between the growth attributes (diameter at breast height, R2 = 0.870; and plant height, R2 = 0.861) and the soil quality (p < 0.01). Therefore, the developed SQI can be used as key indicator for monitoring the restoration potential of D. sissoo growing in marginal and degraded lands and also for adopting suitable interventions to further improve soil quality for multipurpose land restoration programs, thereby attaining land degradation neutrality and United Nations Sustainable Development Goals.

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