Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

Abstract

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.

Geological Mineral Energy and Classification Based on Machine Learning

“…Aravinda and Lin proposed the 3D prediction process of deep mineral resources, namely, "geological information integration -quantitative extraction of metallogenic information -three-dimensional quantitative prediction," and studied the 3D quantitative analysis of geological body shape, quantitative extraction of ore-controlling geological factors, and three-dimensional quantitative prediction of ore bodies [8]. Siomos et al, with the application of three-dimensional comprehensive information metallogenic prediction method, carried out a case study on large-scale three-dimensional metallogenic prediction [9].…”

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

“…Metric learning is a type of mechanism to combine features to compare observations effectively. There are many types of metric learning models, such as stochastic neighbor embedding (SNE) [ 44 ], locally linear embeddings (LLE) [ 45 ], mahalanobis metric for clustering (MMC) [ 46 ], and neighborhood component analysis (NCA) [ 47 ]. The first two are unsupervised, and the latter two are supervised.…”

The Use of Colorimeters to Support Remote Sensing Techniques on Asphalt Pavements

“…Mahalanobis distance [43] measures the distance of a point towards the distribution of points in a multidimensional space [44]. In this study, it deals with the correlations between datasets and their variability.…”