Multivariate weather anomaly detection using DBSCAN clustering algorithm

Weather is highly influential for human life. Weather anomalies describe conditions that are out of the ordinary and need special attention because they can affect various aspects of human life both socially and economically and also can cause natural disasters. Anomaly detection aims to get rid of...

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Vydáno v:Journal of physics. Conference series Ročník 1869; číslo 1; s. 12077
Hlavní autoři: Wibisono, S, Anwar, M T, Supriyanto, A, Amin, I H A
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.04.2021
Témata:
Algorithms
Anomalies
Clustering
Data points
Machine learning
Meteorological data
Natural disasters
Physics
ISSN:1742-6588, 1742-6596
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Popis
Shrnutí:Weather is highly influential for human life. Weather anomalies describe conditions that are out of the ordinary and need special attention because they can affect various aspects of human life both socially and economically and also can cause natural disasters. Anomaly detection aims to get rid of unwanted data (noise, erroneous data, or unwanted data) or to study the anomaly phenomenon itself (unusual but interesting). In the absence of an anomaly-labeled dataset, an unsupervised Machine Learning approach can be utilized to detect or label the anomalous data. This research uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to separate between normal and anomalous weather data by considering multiple weather variables. Then, PCA is used to visualize the clusters. The experimental result had demonstrated that DBSCAN is capable of identifying peculiar data points that are deviating from the ‘normal’ data distribution.
Bibliografie:ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1869/1/012077