An AERONET-based aerosol classification using the Mahalanobis distance

2016 ◽  
Vol 140 ◽  
pp. 213-233 ◽  
Author(s):  
Patrick Hamill ◽  
Marco Giordano ◽  
Carolyne Ward ◽  
David Giles ◽  
Brent Holben
Keyword(s):  
Mahalanobis Distance ◽  
Aerosol Classification

scholarly journals An automatic aerosol classification for earlinet: application and results

2018 ◽  
Vol 176 ◽  
pp. 09012 ◽  
Author(s):  
Nikolaos Papagiannopoulos ◽  
Lucia Mona ◽  
Vassilis Amiridis ◽  
Ioannis Binietoglou ◽  
Giuseppe D’Amico ◽  
...  
Keyword(s):  
Air Quality ◽  
Mahalanobis Distance ◽  
Automatic Classification ◽  
Classification Method ◽  
Weather System ◽  
Network Measurements ◽  
Aerosol Classification ◽  
Aerosol Sources ◽  
Mahalanobis Distance Classifier ◽  
The Impact

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

scholarly journals AN AEROSOL TYPE CLASSIFICATION METHOD BASED ON REMOTE SENSING DATA IN GUANGDONG, CHINA

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 239-243
Author(s):  
Y. C. Zheng ◽  
L. L. Li ◽  
Y. P. Wang
Keyword(s):  
Remote Sensing ◽  
Mahalanobis Distance ◽  
Mixed Type ◽  
Remote Sensing Data ◽  
Classification Method ◽  
Sensing Data ◽  
Aerosol Classification ◽  
Aerosol Types ◽  
Aerosol Properties ◽  
Aerosol Type

Abstract. This paper provides an aerosol classification method based on remote sensing data in Guangdong, China in year 2010 and 2011. Aerosol Optical Depth, Angstrom Exponent and Ultraviolet Aerosol Index, as important properties of aerosols, are introduced into classification. Data of these three aerosol properties are integrated to establish a 3-dimension dataset, and k-means clustering algorithm with Mahalanobis distance is used to find out four clusters of the dataset, which respectively represents four aerosol types of urban-industrial, dust, biomass burning and mixed type. Prior knowledge about the understanding of each aerosol type is involved to associate each cluster with aerosol type. Temporal variation of the aerosol properties shows similarities between these two years. The proportion of aerosol types in different cities of Guangdong Province is also calculated, and result shows that in most cities urban-industrial aerosols takes the largest proportion while the mixed type aerosols takes the second place. Classification results prove that k-means cluster algorithm with Mahalanobis distance is a brief and efficient method for aerosol classification.

scholarly journals Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

2020 ◽  
Vol 12 (6) ◽  
pp. 965 ◽  
Author(s):  
Nikolaos Siomos ◽  
Ilias Fountoulakis ◽  
Athanasios Natsis ◽  
Theano Drosoglou ◽  
Alkiviadis Bais
Keyword(s):  
Machine Learning ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mahalanobis Distance ◽  
Single Scattering ◽  
Training Dataset ◽  
Classification Technique ◽  
Brewer Spectrophotometer ◽  
Aerosol Classification ◽  
Double Monochromator

In this study, we present an aerosol classification technique based on measurements of a double monochromator Brewer spectrophotometer during the period 1998–2017 in Thessaloniki, Greece. A machine learning clustering procedure was applied based on the Mahalanobis distance metric. The classification process utilizes the UV Single Scattering Albedo (SSA) at 340 nm and the Extinction Angstrom Exponent (EAE) at 320–360 nm that are obtained from the spectrophotometer. The analysis is supported by measurements from a CIMEL sunphotometer that were deployed in order to establish the training dataset of Brewer measurements. By applying the Mahalanobis distance algorithm to the Brewer timeseries, we automatically assigned measurements in one of the following clusters: Fine Non Absorbing Mixtures (FNA): 64.7%, Black Carbon Mixtures (BC): 17.4%, Dust Mixtures (DUST): 8.1%, and Mixed: 9.8%. We examined the clustering potential of the algorithm by reclassifying the training dataset and comparing it with the original one and also by using manually classified cases. The typing score of the Mahalanobis algorithm is high for all predominant clusters FNA: 77.0%, BC: 63.9%, and DUST: 80.3% when compared with the training dataset. We obtained high scores as well FNA: 100.0%, BC: 66.7%, and DUST: 83.3% when comparing it with the manually classified dataset. The flags obtained here were applied in the timeseries of the Aerosol Optical Depth (AOD) at 340 nm of the Brewer and the CIMEL in order to compare between the two and also stress the future impact of the proposed clustering technique in climatological studies of the station.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

scholarly journals Asymptotic Inference for Optimal Rerandomization Designs

2021 ◽  
Vol 1 (1) ◽  
pp. 49-58
Author(s):  
Mårten Schultzberg ◽  
Per Johansson
Keyword(s):  
Experimental Design ◽  
Normal Distribution ◽  
Mahalanobis Distance ◽  
Design Strategy ◽  
Sampling Distribution ◽  
Optimal Designs ◽  
Asymptotic Inference ◽  
Optimal Allocations ◽  
Asymptotic Sampling

AbstractRecently a computational-based experimental design strategy called rerandomization has been proposed as an alternative or complement to traditional blocked designs. The idea of rerandomization is to remove, from consideration, those allocations with large imbalances in observed covariates according to a balance criterion, and then randomize within the set of acceptable allocations. Based on the Mahalanobis distance criterion for balancing the covariates, we show that asymptotic inference to the population, from which the units in the sample are randomly drawn, is possible using only the set of best, or ‘optimal’, allocations. Finally, we show that for the optimal and near optimal designs, the quite complex asymptotic sampling distribution derived by Li et al. (2018), is well approximated by a normal distribution.

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