Improvement of fire danger modelling with geographically weighted logistic model

2014 ◽  
Vol 23 (8) ◽  
pp. 1130 ◽  
Author(s):  
Haijun Zhang ◽  
Pengcheng Qi ◽  
Guangmeng Guo
Keyword(s):  
Spatial Autocorrelation ◽  
Logistic Model ◽  
Model Fitting ◽  
Logistic Models ◽  
Principal Component ◽  
Predictive Performance ◽  
Fire Danger ◽  
Local Models ◽  
Global Models ◽  
North East

Global models dominate historical documents on fire danger modelling. However, local variations may exist in the relationships between fire presence and fire-influencing factors. In this study, 50 fire danger models (10 global logistic models and 40 geographically weighted logistic models, i.e. local models), were developed to model daily fire danger in Heilongjiang province in north-east China and cross-validation was performed to evaluate the predictive performance of the various developed models. In modelling, multi-temporal spatial sampling and repeated random sub-sampling were applied to obtain 10 groups of training sub-samples and inner testing sub-samples. For each of the 10 groups of training sub-samples, principal component analysis, in which muticollinearity among variables can be removed, was used to create nine principal components that were then employed as covariates to develop one global logistic model and four geographically weighted logistic models. Compared to global models, all local models showed better model fitting, less spatial autocorrelation of residuals and more desirable modelling of fire presence. In particular, not only was local spatial variation in fire–environment relationships accounted for in the adaptive Gaussian geographically weighted logistic models, but spatial autocorrelation of residuals was significantly reduced to acceptable levels, indicating strong inferential performance.

scholarly journals Myopic Savings Behaviour of Future Polish Pensioners

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 36
Author(s):  
Sonia Buchholtz ◽  
Jan Gąska ◽  
Marek Góra
Keyword(s):  
Logistic Models ◽  
Principal Component ◽  
Social Institutions ◽  
Health Seeking ◽  
Future Health ◽  
Policy Makers ◽  
Second Best ◽  
External Assistance ◽  
Old Age Poverty

Low saving rates combined with low effective retirement age herald old-age poverty. This paper examines the preferred strategies of future Polish pensioners in order to sustain the standard of living in the future. A two-step approach is used: as a first-best strategy, we explore determinants of supplementary saving with binary logistic models; as a second-best strategy, we examine alternative options with principal component analysis. Future retirees rarely accumulate long-term savings, do not use dedicated instruments, and they start to save additionally far too late. Savings are concentrated in wealthier and better educated groups. Such myopia is governed by their political stance and not by awareness of dire prospects. Second-best strategies are based on optimistic assumptions about future health (seeking for additional jobs), on the assumed generosity of acquaintances or social institutions (relying on external assistance), or on rebelling. Given the increasing political power of elder generations, balancing the interests of workers and retirees will be an increasingly difficult task for policy makers.

Examining Industrial Clusters from an Ecological Perspective: Evidences from North-East China

2012 ◽  
Vol 13 (2) ◽  
pp. 60-84
Author(s):  
Ge Li ◽  
Ramudu Bhanugopan ◽  
Alan Fish
Keyword(s):  
Differential Equations ◽  
Logistic Model ◽  
Industrial Clusters ◽  
Ecological Perspective ◽  
Positive Outcomes ◽  
Republic Of China ◽  
North East ◽  
The North ◽  
Regional Cluster ◽  
Key Factor

Industrial clusters are increasingly seen as essential in effectively combining, developing and enhancing like-minded businesses. Industrial clusters irrespective of their location are providing positive outcomes for ecological derivatives in supporting effective industrial developments. This perspective is addressed within this paper via employing the ‘Logistic Model of Ecology’; through the application of differential equations. This paper explains key interspecies relationships; competition, predation and symbiosis, operating within a regional cluster in the Jilin Province in the north-east of The Peoples’ Republic of China. The paper draws the conclusion that ‘intense competition’ is the key factor contributing to the successful existence of the cluster.

scholarly journals The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Thais Destefani Ribeiro ◽  
Taciana Villela Savian ◽  
Tales Jesus Fernandes ◽  
Joel Augusto Muniz
Keyword(s):  
Logistic Model ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Fruit Weight ◽  
Coefficient Of Determination ◽  
Pear Fruit ◽  
Asian Pear ◽  
Residual Correlations

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.

scholarly journals The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

2017 ◽  
Vol 10 (4) ◽  
pp. 1679-1701 ◽  
Author(s):  
Silvia Caldararu ◽  
Drew W. Purves ◽  
Matthew J. Smith
Keyword(s):  
Environmental Changes ◽  
Crop Yields ◽  
Vegetation Indices ◽  
Model Fitting ◽  
Model Performance ◽  
Predictive Performance ◽  
Crop Model ◽  
Model Fit ◽  
General Process ◽  
Fitting Algorithm

Abstract. Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data–space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

scholarly journals Factors Associated with Acute Community-Acquired Pyelonephritis Caused by Extended-Spectrum β-Lactamase-Producing Escherichia coli

2021 ◽  
Vol 10 (21) ◽  
pp. 5192
Author(s):  
Mónica Romero Nieto ◽  
Sara Maestre Verdú ◽  
Vicente Gil ◽  
Carlos Pérez Barba ◽  
Jose Antonio Quesada Rico ◽  
...  
Keyword(s):  
Roc Curve ◽  
Goodness Of Fit ◽  
Logistic Models ◽  
Predictive Performance ◽  
Demographic Characteristics ◽  
E Coli ◽  
Factors Associated ◽  
Extended Spectrum ◽  
Male Sex ◽  
Tract Infections

This study aimed to identify the factors associated with the presence of extended-spectrum ß-lactamase-(ESBL) in patients with acute community-acquired pyelonephritis (APN) caused by Escherechia coli (E. coli), with a view of optimising empirical antibiotic therapy in this context. We performed a retrospective analysis of patients with community-acquired APN and confirmed E. coli infection, collecting data related to demographic characteristics, comorbidities, and treatment. The associations of these factors with the presence of ESBL were quantified by fitting multivariate logistic models. Goodness-of-fit and predictive performance were measured using the ROC curve. We included 367 patients of which 51 presented with ESBL, of whom 90.1% had uncomplicated APN, 56.1% were women aged ≤55 years, 33.5% had at least one mild comorbidity, and 12% had recently taken antibiotics. The prevalence of ESBL-producing E. coli was 13%. In the multivariate analysis, the factors independently associated with ESBL were male sex (OR 2.296; 95% CI 1.043–5.055), smoking (OR 4.846, 95% CI 2.376–9.882), hypertension (OR 3.342, 95% CI 1.423–7.852), urinary incontinence (OR 2.291, 95% CI 0.689–7.618) and recurrent urinary tract infections (OR 4.673, 95% CI 2.271–9.614). The area under the ROC curve was 0.802 (IC 95% 0.7307–0.8736), meaning our model can correctly classify an individual with ESBL-producing E. coli infection in 80.2% of cases.

scholarly journals Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2298 ◽  
Author(s):  
Wudong Li ◽  
Weiping Jiang ◽  
Zhao Li ◽  
Hua Chen ◽  
Qusen Chen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Missing Data ◽  
Principal Component ◽  
Component Analysis ◽  
Common Mode ◽  
Variational Bayesian ◽  
North East ◽  
Position Time Series ◽  
Gnss Position Time Series

Removal of the common mode error (CME) is very important for the investigation of global navigation satellite systems’ (GNSS) error and the estimation of an accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods normally process the evenly spaced time series without missing data. In this article, we present the variational Bayesian principal component analysis (VBPCA) to estimate and extract CME from the incomplete GNSS position time series. The VBPCA method can naturally handle missing data in the Bayesian framework and utilizes the variational expectation-maximization iterative algorithm to search each principal subspace. Moreover, it could automatically select the optimal number of principal components for data reconstruction and avoid the overfitting problem. To evaluate the performance of the VBPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California were selected. Compared to previous approaches, VBPCA could achieve better performance with lower CME relative errors when more missing data exists. Since the first principal component (PC) extracted by VBPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.43, 0.46, and 0.38 to 0.11, 0.10, and 0.08, for the north, east, and up (NEU) components, respectively. The root mean square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with average reductions of 27.11%, 28.15%, and 23.28% for the former, and 49.90%, 54.56%, and 49.75% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with average uncertainty reductions of 51.95%, 57.31%, and 49.92% for the NEU components, respectively. All these results indicate that the VBPCA method is an alternative and efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the VBPCA implementation.

scholarly journals Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach

2019 ◽  
Vol 35 (24) ◽  
pp. 5257-5263 ◽  
Author(s):  
Camilo L M Morais ◽  
Marfran C D Santos ◽  
Kássio M G Lima ◽  
Francis L Martin
Keyword(s):  
Biomedical Applications ◽  
Sample Selection ◽  
Principal Component ◽  
Predictive Performance ◽  
Model Construction ◽  
Classification Models ◽  
Data Splitting ◽  
Random Mutation ◽  
Linear Discriminant ◽  
Test Sets

Abstract Motivation Data splitting is a fundamental step for building classification models with spectral data, especially in biomedical applications. This approach is performed following pre-processing and prior to model construction, and consists of dividing the samples into at least training and test sets; herein, the training set is used for model construction and the test set for model validation. Some of the most-used methodologies for data splitting are the random selection (RS) and the Kennard-Stone (KS) algorithms; here, the former works based on a random splitting process and the latter is based on the calculation of the Euclidian distance between the samples. We propose an algorithm called the Morais-Lima-Martin (MLM) algorithm, as an alternative method to improve data splitting in classification models. MLM is a modification of KS algorithm by adding a random-mutation factor. Results RS, KS and MLM performance are compared in simulated and six real-world biospectroscopic applications using principal component analysis linear discriminant analysis (PCA-LDA). MLM generated a better predictive performance in comparison with RS and KS algorithms, in particular regarding sensitivity and specificity values. Classification is found to be more well-equilibrated using MLM. RS showed the poorest predictive response, followed by KS which showed good accuracy towards prediction, but relatively unbalanced sensitivities and specificities. These findings demonstrate the potential of this new MLM algorithm as a sample selection method for classification applications in comparison with other regular methods often applied in this type of data. Availability and implementation MLM algorithm is freely available for MATLAB at https://doi.org/10.6084/m9.figshare.7393517.v1.

scholarly journals Growth in weight and of some tissues in the bullfrog: fitting nonlinear models during the fattening phase

2014 ◽  
Vol 38 (6) ◽  
pp. 598-606 ◽  
Author(s):  
Marcelo Maia Pereira ◽  
Cleber Fernando Menegasso Mansano ◽  
Edney Pereira da Silva ◽  
Marta Verardino De Stéfani
Keyword(s):  
Food Intake ◽  
Logistic Model ◽  
Fat Body ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Live Weight ◽  
Initial Weight ◽  
Expected Values ◽  
Growth Of Animals ◽  
Cumulative Food Intake

Knowledge of the growth of animals is important so that zootechnical activity can be more accurate and sustainable. The objective of this study was to describe the live weight, development of liver tissue and fat body, leg growth, and cumulative food intake of bullfrogs during the fattening phase using nonlinear models. A total of 2,375 bullfrog froglets with an initial weight of 7.03 ± 0.16 g were housed in five fattening pens (12 m²). Ten samplings were performed at intervals of 14 days to obtain the variables studied. These data were used to estimate the parameters of Gompertz and logistic models as a function of time. The estimated values of weight (Wm) and food intake (FIm) at maturity and time when the growth rate is maximum (t*) were closer to expected values when the logistic model was used. The Wm values for live weight and liver, adipose and leg weights and the FIm value for food intake were 343.7, 15.7, 19.6, 96.03 and 369.3 g, respectively, with t* at 109, 98, 105, 109 and 107 days. Therefore, the logistic model was the best model to estimate the growth and food intake of bullfrogs during the fattening phase.

Modeling the Spatial Variation in U.S. Airfares Utilizing Geographically Weighted Regression

2014 ◽  
Vol 5 (4) ◽  
pp. 54-71
Author(s):  
Hilton A. Cordoba ◽  
Russell L. Ivy
Keyword(s):  
Spatial Variation ◽  
Influencing Factors ◽  
Geographically Weighted Regression ◽  
Regression Models ◽  
Yield Management ◽  
Weighted Regression ◽  
Local Models ◽  
Multivariate Approach ◽  
Global Models ◽  
Economic Changes

Modeling airline fares is quite challenging due to the constantly changing fare structure of the airlines in response to competitors, yield management principles, and a variety of political and economic changes, and has become more complex since deregulation. This paper attempts to add to the literature by providing a more in-depth look at fare structure using a multivariate approach. A total 6,200 routes between 80 primary U.S. airports are analyzed using linear and geographically weighted regression models. The results from the global models reinforce some of the expectations mentioned in the literature, while the local models provide an opportunity to analyze the spatial variation of influencing factors and predictability.

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