scholarly journals Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3466
Author(s):  
Gustavo de Araújo Carvalho ◽  
Peter J. Minnett ◽  
Nelson F. F. Ebecken ◽  
Luiz Landau
Keyword(s):  
Oil Spills ◽  
Location Parameter ◽  
Width Ratio ◽  
Cube Root ◽  
Data Transformations ◽  
Linear Discriminant ◽  
Size Information ◽  
Surface Ocean ◽  
Location Parameters ◽  
Binary Classifiers

Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log10). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique.

scholarly journals Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

2019 ◽  
Vol 11 (14) ◽  
pp. 1652 ◽  
Author(s):  
Gustavo de Araújo Carvalho ◽  
Peter J. Minnett ◽  
Eduardo T. Paes ◽  
Fernando P. de Miranda ◽  
Luiz Landau
Keyword(s):  
Discriminant Analysis ◽  
Gulf Of Mexico ◽  
Linear Discriminant Analysis ◽  
Petroleum Industry ◽  
Cube Root ◽  
Data Transformations ◽  
Linear Discriminant ◽  
Oil Slick ◽  
Category Discrimination ◽  
The Impact

A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.

scholarly journals The Issue of Using Ordinal Quantities to Estimate the Vulnerability of Seabirds to Oil Spills

2020 ◽  
Vol 8 (12) ◽  
pp. 1026
Author(s):  
Anatoly Shavykin ◽  
Andrey Karnatov
Keyword(s):  
Initial Data ◽  
Oil Spills ◽  
Measurement Theory ◽  
Original Data ◽  
Ordinal Scale ◽  
Arithmetic Operations ◽  
Data Transformations ◽  
Negative Effect ◽  
Methodological Approaches ◽  
Relative Vulnerability

Oil spills can have a serious negative effect on seabirds. Numerous studies have been carried out for relative vulnerability assessment of seabirds to oil, with the majority of such works based on ordinal quantities. This study aims to assess (from the aspect of measurement theory) the methodological approaches used for calculating the vulnerability of seabirds to oil spills, and corresponding conclusions. We assess several well-known works on the vulnerability of seabirds (1979–2004). We consider the effect on derived conclusions of (a) monotonic initial data transformations on an ordinal scale, (b) multiplication operations on the same scale, and (c) the replacement of initial metric data to ordinal. Our results show the following: (a) the conclusions for arithmetic operations may not be saved with permissible monotonic transformations of ordinal quantities; (b) partially uncertain results can be obtained with arithmetic operations on an ordinal scale as compared with metric; (c) the replacement of metric values to scores changes the real relationships among initial data and affects the final result. Thus, conclusions in works which use arithmetic operations with ordinal quantities cannot be considered to be justified and correct, since they are based on unacceptable operations and, quite often, on the distorted original data.

scholarly journals Refined Analysis of RADARSAT-2 Measurements to Discriminate Two Petrogenic Oil-Slick Categories: Seeps versus Spills

2018 ◽  
Vol 6 (4) ◽  
pp. 153 ◽  
Author(s):  
Gustavo Carvalho ◽  
Peter Minnett ◽  
Eduardo Paes ◽  
Fernando de Miranda ◽  
Luiz Landau
Keyword(s):  
Oil Spills ◽  
Surface Expression ◽  
Attribute Selection ◽  
Data Repository ◽  
Cube Root ◽  
Surveillance Systems ◽  
Linear Transformations ◽  
Discrimination Power ◽  
Oil Slick ◽  
Synthetic Aperture Radars

Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars.

scholarly journals A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Tao Geng ◽  
John Q. Gan ◽  
Matthew Dyson ◽  
Chun SL Tsui ◽  
Francisco Sepulveda
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Algorithms ◽  
Single Trial ◽  
Linear Discriminant ◽  
The Subject ◽  
Offline Analysis ◽  
Binary Classifiers ◽  
Novel Design ◽  
Mental Tasks

A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.

Selection Procedures for Location Parameters Based on Two-Sample U-Statistics

1992 ◽  
Vol 42 (3-4) ◽  
pp. 201-220 ◽  
Author(s):  
Narinder Kumar ◽  
Amar Nath Gill ◽  
Gobind P . Mehta
Keyword(s):  
Cumulative Distribution Function ◽  
Subset Selection ◽  
Location Parameter ◽  
Asymptotic Relative Efficiency ◽  
Cumulative Distribution ◽  
Absolutely Continuous ◽  
Selection Procedures ◽  
Secondary 62H10 ◽  
U Statistics ◽  
Location Parameters

Let π1, ... , πk be k independent populations and let Fi ( x)= F( x - θi) be the absolutely continuous cumulative distribution function (cdf) of the i-th population indexed by the location parameter θi; i=1,,.... k. A class of subset selection procedures based on sub-sample extrema for unequal sample sizes is proposed for the problem of selecting a subset from ( π1, .... πk) which contains the population with largest location parameter. The proposed subset selection procedures are then compared with the subset selection procedures of Hsu (1981) in the sense of Pitman ARE (asymptotic relative efficiency). It is shown that these procedures can approximately be implemented with the help of existing tables and sample size sufficient for their implementation, based on simulation results, is discussed. AMS (1980) Subject Classification: Primary 62F07; Secondary 62H10

scholarly journals On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 706 ◽  
Author(s):  
Shahid Hussain ◽  
Sun Mei ◽  
Muhammad Riaz ◽  
Saddam Akber Abbasi
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Charts ◽  
Auxiliary Information ◽  
Location Parameter ◽  
Attractive Alternative ◽  
Location Parameters ◽  
The Mean ◽  
Two Phases

A control chart is often used to monitor the industrial or services processes to improve the quality of the products. Mostly, the monitoring of location parameters, both in Phase I and Phase II, is done using a mean control chart with the assumption that the process is free from outliers or the estimators are correctly estimated from in-control samples. Generally, there are question marks about such kind of narratives. The performance of the mean chart is highly affected in the presence of outliers. Therefore, the median chart is an attractive alternative to the mean chart in this situation. The control charts are usually implemented in two phases: Phase I (retrospective) and Phase II (prospective/monitoring). The efficiency of any control chart in Phase II depends on the accuracy of control limits obtained from Phase I. The current study focuses on the Phase I analysis of location parameters using median control charts. We examined the performance of different auxiliary information-based median control charts and compared the results with the usual median chart. Standardized variance and relative efficacy are used as performance measures to evaluate the efficiency of median estimators. Moreover, the probability to signal measure is used to evaluate the performance of proposed control charts to detect any potential changes in the process. The results revealed that the proposed auxiliary information based median control charts perform better in Phase I analysis. In addition, a practical illustration of an industrial scenario demonstrated the significance of the proposed control charts, in which the monitoring of concrete compressive strength is emphasized.

scholarly journals Data Mining Techniques for Analysing Employment Data

2019 ◽  
Vol 9 (2) ◽  
pp. 555-556
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Classification Tree ◽  
Predictive Modelling ◽  
Testing Procedure ◽  
Support Vector ◽  
Linear Discriminant ◽  
Rigorous Testing ◽  
Data Partitions ◽  
Binary Classifiers

This paper proposes a methodology that uses a large-scale employment dataset in order to explore which factors affect employment and how. The proposed methodology is a combination of predictive modelling, variable significance analysis, and VEC analysis. Modelling is based on logistic regression, linear discriminant analysis, neural network, classification tree, and support vector machine. Following the CRISP-DM standard process model, we train binary classifiers optimising their hyper-parameters and measure their performance by prediction accuracy, ROC analysis, and AUC. Using sensitivity analysis, we rank the variable significance in order to identify and measure factors of employment. Using VEC analysis, we further explore how values of those factors affect employment. Findings show that best performing models are neural networks and support vector machines with preference to the latter for quality of VEC. Experiments also suggest that education and age are primary contributors for correct classification with specific value distribution, discussed in the paper. All results were validated using a rigorous testing procedure that involves training, validation, and test data partitions and a combination of multiple runs along with three-fold cross-validation. This study addresses some gaps in previous research publications, which lack quantification of the conclusions made.

Bandgaps characteristics of the periodical slit metal tubes with backstraps

2017 ◽  
Vol 31 (15) ◽  
pp. 1750125 ◽  
Author(s):  
Kai Bao ◽  
Tianning Chen ◽  
Xiaopeng Wang ◽  
Ailing Song ◽  
Lele Wan
Keyword(s):  
Acoustic Waves ◽  
Mass Density ◽  
Phononic Crystal ◽  
Location Parameter ◽  
Slit Width ◽  
The Finite Element Method ◽  
Frequency Range ◽  
Transmission Coefficients ◽  
Helmholtz Resonators ◽  
Location Parameters

In this paper, a new two-dimensional (2D) phononic crystal structure composed of periodic slit metal tubes, in which the unit cell consists of straight or curved backstraps, is proposed, and the propagation characteristics of acoustic waves in this structure are theoretically investigated. Using the finite-element method, we calculate the dispersion relations and transmission coefficients of this structure. The results show that, in contrast to the only slit metal tubes, the periodic slit metal tubes with straight or curved backstraps are proved to display band gaps (BGs) at much lower frequency range. Meanwhile, the effect of the slit width of the backstraps on the BGs is investigated. The results show that the positions and widths of the BGs can be effectively modulated by the backstraps without changing the mass density or lattice constant of the material. The lowest frequency falls by about 200 Hz. Moreover, we investigated how the BGs are affected by the location parameter of the backstraps, finding that the acoustic BGs are sensitive to the location parameter of the backstraps. Numerical results show that BGs are significantly dependent upon the slit width and location parameters of the backstraps. The BGs are optimized because, the effect of the Helmholtz resonators of the slit tube is strengthened and changed when the location and slit width of the backstraps change. These results provide a good reference for optimizing BGs, generating filters and designing devices.

Predicting Shoppers' Continuous Buying Intention Using Mobile Apps

2018 ◽  
Vol 9 (3) ◽  
pp. 69-83
Author(s):  
Sanjeev Prashar ◽  
Priyanka Gupta ◽  
Chandan Parsad ◽  
T. Sai Vijay
Keyword(s):  
Decision Tree ◽  
Predictive Accuracy ◽  
Mobile Apps ◽  
Neutral Network ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Linear Discriminant ◽  
Artificial Neutral Network ◽  
Binary Classifiers ◽  
Buying Behaviour

The rapid penetration of smartphones and consumers' increased usage/dependence on mobile applications (apps) has ushered favorable opportunities for retailers as well as shoppers. The traditional brick-and-mortar as well as online retailers must attract shoppers to use mobile shopping apps. For this, it is pertinent for retailers to predict users' continuous intention to buy through apps. To address this question, the present study has applied four prominent binary classifiers - logit regression, linear discriminant analysis, artificial neutral network and decision tree analysis to develop predictive models. Findings of the study shall help the marketers in accurately forecasting shoppers' buying behaviour. Various indices have been used to check the predictive accuracy of four techniques. The outcome of the study shows that the models developed using decision tree analysis and artificial neutral network provide better results in predicting consumers' continuous intention to buy through app. Based on the findings, the paper has also provided implications for the retailers.

scholarly journals The multivariate multisample nonparametric rank statistics for the location alternatives

2017 ◽  
Vol 46 (1) ◽  
pp. 3-13
Author(s):  
Hidetoshi Murakami
Keyword(s):  
Multivariate Data ◽  
Location Parameter ◽  
Nonparametric Statistics ◽  
Nonparametric Tests ◽  
Rank Statistics ◽  
Testing Problem ◽  
Population Distributions ◽  
Location Parameters ◽  
Wilks Lambda ◽  
Scientific Fields

Multisample testing problems are among the most important topics in nonparametric statistics. Various nonparametric tests have been proposed for multisample testing problems involving location parameters, and the analysis of multivariate data is important in many scientific fields. One type of multivariate multisample testing problem based on Jureckova-Kalina-type rank of distance is discussed in this paper. A multivariate Kruskal-Wallis-type statistic is proposed for testing the location parameter with both equal and unequal sample sizes. Simulations are used to compare the power of proposed nonparametric statistics with the Wilks' lambda, the Pillai's trace and the Lawley-Hotelling trace for various population distributions. 

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