scholarly journals Comparison and selection of growth models using the Schnute model

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 188-196 ◽  
Author(s):  
Y. Lei ◽  
S. Y Zhang
Keyword(s):  
Functional Form ◽  
Growth Models ◽  
A Priori ◽  
Data Sets ◽  
Forest Species ◽  
Empirical Relationships ◽  
Data Set ◽  
Schnute Model ◽  
Functional Forms ◽  
Selection Of

Forestmodellers have long faced the problem of selecting an appropriate mathematical model to describe tree ontogenetic or size-shape empirical relationships for tree species. A common practice is to develop many models (or a model pool) that include different functional forms, and then to select the most appropriate one for a given data set. However, this process may impose subjective restrictions on the functional form. In this process, little attention is paid to the features (e.g. asymptote and inflection point rather than asymptote and nonasymptote) of different functional forms, and to the intrinsic curve of a given data set. In order to find a better way of comparing and selecting the growth models, this paper describes and analyses the characteristics of the Schnute model. This model has both flexibility and versatility that have not been used in forestry. In this study, the Schnute model was applied to different data sets of selected forest species to determine their functional forms. The results indicate that the model shows some desirable properties for the examined data sets, and allows for discerning the different intrinsic curve shapes such as sigmoid, concave and other curve shapes. Since no suitable functional form for a given data set is usually known prior to the comparison of candidate models, it is recommended that the Schnute model be used as the first step to determine an appropriate functional form of the data set under investigation in order to avoid using a functional form a priori.

Validation of Sentinel-5P TROPOMI tropospheric NO2 with airborne imaging, ground-based stationary, and mobile DOAS measurements from the S5P-VAL-DE-Ruhr campaign

2021 ◽  
Author(s):  
Kezia Lange ◽  
Andreas C. Meier ◽  
Michel Van Roozendael ◽  
Thomas Wagner ◽  
Thomas Ruhtz ◽  
...  
Keyword(s):  
A Priori ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Ruhr Area ◽  
Spatial And Temporal Heterogeneity ◽  
Western Germany ◽  
Airborne Imaging ◽  
Urban Character ◽  
One Year

<p>Airborne imaging DOAS and ground-based stationary and mobile DOAS measurements were conducted during the ESA funded S5P-VAL-DE-Ruhr campaign in September 2020 in the Ruhr area. The Ruhr area is located in Western Germany and is a pollution hotspot in Europe with urban character as well as large industrial emitters. The measurements are used to validate data from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) with focus on the NO<sub>2</sub> tropospheric vertical column product.</p><p>Seven flights were performed with the airborne imaging DOAS instrument, AirMAP, providing continuous maps of NO<sub>2</sub> in the layers below the aircraft. These flights cover many S5P ground pixels within an area of about 40 km side length and were accompanied by ground-based stationary measurements and three mobile car DOAS instruments. Stationary measurements were conducted by two Pandora, two zenith-sky and two MAX-DOAS instruments distributed over three target areas, partly as long-term measurements over a one-year period.</p><p>Airborne and ground-based measurements were compared to evaluate the representativeness of the measurements in time and space. With a resolution of about 100 x 30 m<sup>2</sup>, the AirMAP data creates a link between the ground-based and the TROPOMI measurements with a resolution of 3.5 x 5.5 km<sup>2</sup> and is therefore well suited to validate TROPOMI's tropospheric NO<sub>2</sub> vertical column.</p><p>The measurements on the seven flight days show strong variability depending on the different target areas, the weekday and meteorological conditions. We found an overall low bias of the TROPOMI operational NO<sub>2</sub> data for all three target areas but with varying magnitude for different days. The campaign data set is compared to custom TROPOMI NO<sub>2</sub> products, using different auxiliary data, such as albedo or a priori vertical profiles to evaluate the influence on the TROPOMI data product. Analyzing and comparing the different data sets provides more insight into the high spatial and temporal heterogeneity in NO<sub>2</sub> and its impact on satellite observations and their validation.</p>

Marlim R3D: A realistic model for controlled-source electromagnetic simulations — Phase 2: The controlled-source electromagnetic data set

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. E293-E299
Author(s):  
Jorlivan L. Correa ◽  
Paulo T. L. Menezes
Keyword(s):  
A Priori ◽  
Synthetic Data ◽  
Realistic Model ◽  
Earth Model ◽  
Data Sets ◽  
Data Set ◽  
Geoelectric Model ◽  
Controlled Source ◽  
The North ◽  
Electromagnetic Simulations

Synthetic data provided by geoelectric earth models are a powerful tool to evaluate a priori a controlled-source electromagnetic (CSEM) workflow effectiveness. Marlim R3D (MR3D) is an open-source complex and realistic geoelectric model for CSEM simulations of the postsalt turbiditic reservoirs at the Brazilian offshore margin. We have developed a 3D CSEM finite-difference time-domain forward study to generate the full-azimuth CSEM data set for the MR3D earth model. To that end, we fabricated a full-azimuth survey with 45 towlines striking the north–south and east–west directions over a total of 500 receivers evenly spaced at 1 km intervals along the rugged seafloor of the MR3D model. To correctly represent the thin, disconnected, and complex geometries of the studied reservoirs, we have built a finely discretized mesh of [Formula: see text] cells leading to a large mesh with a total of approximately 90 million cells. We computed the six electromagnetic field components (Ex, Ey, Ez, Hx, Hy, and Hz) at six frequencies in the range of 0.125–1.25 Hz. In our efforts to mimic noise in real CSEM data, we summed to the data a multiplicative noise with a 1% standard deviation. Both CSEM data sets (noise free and noise added), with inline and broadside geometries, are distributed for research or commercial use, under the Creative Common License, at the Zenodo platform.

The Gamma Test

2011 ◽  
pp. 142-167 ◽  
Author(s):  
Antonia J. Jones ◽  
Dafydd Evans ◽  
Steve Margetts ◽  
Peter J. Durrant
Keyword(s):  
Predictive Models ◽  
Time Complexity ◽  
Numerical Data ◽  
Data Sets ◽  
Analysis Tool ◽  
Embedding Dimension ◽  
Gamma Test ◽  
Data Set ◽  
Modelling Analysis ◽  
Selection Of

The Gamma Test is a non-linear modelling analysis tool that allows us to quantify the extent to which a numerical input/output data set can be expressed as a smooth relationship. In essence, it allows us to efficiently calculate that part of the variance of the output that cannot be accounted for by the existence of any smooth model based on the inputs, even though this model is unknown. A key aspect of this tool is its speed: the Gamma Test has time complexity O(Mlog M), where M is the number of datapoints. For data sets consisting of a few thousand points and a reasonable number of attributes, a single run of the Gamma Test typically takes a few seconds. In this chapter we will show how the Gamma Test can be used in the construction of predictive models and classifiers for numerical data. In doing so, we will demonstrate the use of this technique for feature selection, and for the selection of embedding dimension when dealing with a time-series.

A model of the distribution and metabolism of corticotropin-releasing factor

1988 ◽  
Vol 254 (1) ◽  
pp. E104-E112
Author(s):  
B. Candas ◽  
J. Lalonde ◽  
M. Normand
Keyword(s):  
Measurement Errors ◽  
Compartment Model ◽  
Corticotropin Releasing Factor ◽  
Data Sets ◽  
Data Set ◽  
Rapid Injection ◽  
Three Compartment Model ◽  
Rival Models ◽  
Sampling Schedule ◽  
Selection Of

The aim of this study is the selection of the number of compartments required for a model to represent the distribution and metabolism of corticotropin-releasing factor (CRF) in rats. The dynamics of labeled rat CRF were measured in plasma for seven rats after a rapid injection. The sampling schedule resulted from the combination of the two D-optimal sampling sets of times corresponding to both rival models. This protocol improved the numerical identifiability of the parameters and consequently facilitated the selection of the relevant model. A three-compartment model fits adequately to the seven individual dynamics and better represents four of them compared with the lower-order model. It was demonstrated, using simulations in which the measurement errors and the interindividual variability of the parameters are included, that his four-to-seven ratio of data sets is consistent with the relevance of the three-compartment model for every individual kinetic data set. Kinetic and metabolic parameters were then derived for each individual rat, their values being consistent with the prolonged effects of CRF on pituitary-adrenocortical secretion.

A Growth Model for Multilevel Ordinal Data

2005 ◽  
Vol 30 (4) ◽  
pp. 369-396 ◽  
Author(s):  
Eisuke Segawa
Keyword(s):  
Latent Variable ◽  
Ordinal Data ◽  
Linear Models ◽  
Growth Models ◽  
Simulated Data ◽  
Real Data ◽  
Analytic Structure ◽  
Data Sets ◽  
Data Set ◽  
Time Points

Multi-indicator growth models were formulated as special three-level hierarchical generalized linear models to analyze growth of a trait latent variable measured by ordinal items. Items are nested within a time-point, and time-points are nested within subject. These models are special because they include factor analytic structure. This model can analyze not only data with item- and time-level missing observations, but also data with time points freely specified over subjects. Furthermore, features useful for longitudinal analyses, “autoregressive error degree one” structure for the trait residuals and estimated time-scores, were included. The approach is Bayesian with Markov Chain and Monte Carlo, and the model is implemented in WinBUGS. They are illustrated with two simulated data sets and one real data set with planned missing items within a scale.

scholarly journals Age estimation and the best growth model selection of the tentacled blenny Parablennius tentacularis (Brünnich, 1768) in the southeastern Black Sea

2021 ◽  
Vol 38 (2) ◽  
pp. 229-236
Author(s):  
Ayşe Van ◽  
Aysun Gümüş ◽  
Melek Özpiçak ◽  
Serdar Süer
Keyword(s):  
Growth Model ◽  
Black Sea ◽  
Growth Parameters ◽  
Growth Models ◽  
Information Criterion ◽  
Current Data ◽  
Logistic Growth ◽  
Data Set ◽  
Frequency Distributions ◽  
Selection Of

By the study's coverage, 522 individuals of tentacled blenny (Parablennius tentacularis (Brünnich, 1768)), were caught with the bottom trawl operations (commercial fisheries and scientific field surveys) between May 2010 and March 2012 from the southeastern Black Sea. The size distribution range of the sample varied between 4.8-10.8 cm. The difference between sex length (K-S test, Z=3.729, P=0.000) and weight frequency distributions (K-S test, Z=3.605, P=0.000) was found to be statistically significant. The length-weight relationship models were defined as isometric with W = 0.009L3.034 in male individuals and positive allometric with W = 0.006L3.226 in female individuals. Otolith and vertebra samples were compared for the selection of the most accurate hard structure that can be used to determine the age. Otolith was chosen as the most suitable hard structure. The current data set was used to predict the best growth model. For this purpose, the growth parameters were estimated with the widely used von Bertalanffy, Gompertz and Logistic growth functions. Akaike's Information Criterion (AIC), Lmak./L∞ ratio, and R2 criteria were used to select the most accurate growth models established through these functions. Model averaged parameters were calculated with multi-model inference (MMI): L'∞ = 15.091 cm, S.E. (L'∞) = 3.966, K'= 0.232 year-1, S.E. (K') = 0.122.

scholarly journals Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Romana Haneef ◽  
Sofiane Kab ◽  
Rok Hrzic ◽  
Sonsoles Fuentes ◽  
Sandrine Fosse-Edorh ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Test Data ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Data Sets ◽  
Data Set ◽  
Linear Discriminant ◽  
Final Data ◽  
Selection Of

Abstract Background The use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years. Methods We selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm. Results The final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66–0.68]. Conclusions Supervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions.

scholarly journals A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

2019 ◽  
Vol 5 (10) ◽  
pp. 2120-2130 ◽  
Author(s):  
Suraj Kumar ◽  
Thendiyath Roshni ◽  
Dar Himayoun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Data Set ◽  
Discharge Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Emotional Neural Network ◽  
Selection Of

Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014.  Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies.  The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.

Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps

1999 ◽  
Vol 09 (03) ◽  
pp. 195-202 ◽  
Author(s):  
JOSÉ ALFREDO FERREIRA COSTA ◽  
MÁRCIO LUIZ DE ANDRADE NETTO
Keyword(s):  
A Priori ◽  
Multivariate Data ◽  
Feature Space ◽  
Search Space ◽  
Data Sets ◽  
Self Organizing Maps ◽  
Data Set ◽  
Number Of Clusters ◽  
Using Data ◽  
Self Organizing

Determining the structure of data without prior knowledge of the number of clusters or any information about their composition is a problem of interest in many fields, such as image analysis, astrophysics, biology, etc. Partitioning a set of n patterns in a p-dimensional feature space must be done such that those in a given cluster are more similar to each other than the rest. As there are approximately [Formula: see text] possible ways of partitioning the patterns among K clusters, finding the best solution is very hard when n is large. The search space is increased when we have no a priori number of partitions. Although the self-organizing feature map (SOM) can be used to visualize clusters, the automation of knowledge discovery by SOM is a difficult task. This paper proposes region-based image processing methods to post-processing the U-matrix obtained after the unsupervised learning performed by SOM. Mathematical morphology is applied to identify regions of neurons that are similar. The number of regions and their labels are automatically found and they are related to the number of clusters in a multivariate data set. New data can be classified by labeling it according to the best match neuron. Simulations using data sets drawn from finite mixtures of p-variate normal densities are presented as well as related advantages and drawbacks of the method.

Structures of copper(II) and manganese(II) di(hydrogen malonate) dihydrate; effects of intensity profile truncation and background modelling on structure models

2001 ◽  
Vol 57 (4) ◽  
pp. 497-506 ◽  
Author(s):  
A. T. H. Lenstra ◽  
O. N. Kataeva
Keyword(s):  
Crystal Structures ◽  
Truncation Error ◽  
High Order ◽  
Data Sets ◽  
Data Set ◽  
Background Modelling ◽  
Local Background ◽  
Model Robustness ◽  
Selection Of ◽  
Low Order

The crystal structures of the title compounds were determined with net intensities I derived via the background–peak–background procedure. Least-squares optimizations reveal differences between the low-order (0 < s < 0.7 Å−1) and high-order (0.7 < s < 1.0 Å−1) structure models. The scale factors indicate discrepancies of up to 10% between the low-order and high-order reflection intensities. This observation is compound independent. It reflects the scan-angle-induced truncation error, because the applied scan angle (0.8 + 2.0 tan θ)° underestimates the wavelength dispersion in the monochromated X-ray beam. The observed crystal structures show pseudo-I-centred sublattices for three of its non-H atoms in the asymmetric unit. Our selection of observed intensities (I > 3σ) stresses that pseudo-symmetry. Model refinements on individual data sets with (h + k + l) = 2n and (h + k + l) = 2n + 1 illustrate the lack of model robustness caused by that pseudo-symmetry. To obtain a better balanced data set and thus a more robust structure we decided to exploit background modelling. We described the background intensities B(\displaystyle\mathrel{\mathop H^{\rightharpoonup}}) with an 11th degree polynomial in θ. This function predicts the local background b at each position \displaystyle\mathrel{\mathop H^{\rightharpoonup}} and defines the counting statistical distribution P(B), in which b serves as average and variance. The observation R defines P(R). This leads to P(I) = P(R)/P(B) and thus I = R − b and σ2(I) = I so that the error σ(I) is background independent. Within this framework we reanalysed the structure of the copper(II) derivative. Background modelling resulted in a structure model with an improved internal consistency. At the same time the unweighted R value based on all observations decreased from 10.6 to 8.4%. A redetermination of the structure at 120 K concluded the analysis.

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