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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Published in:Journal of physics. Conference series Vol. 1869; no. 1; p. 12077
Main Authors: Wibisono, S, Anwar, M T, Supriyanto, A, Amin, I H A
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.04.2021
Subjects:
Algorithms
Anomalies
Clustering
Data points
Machine learning
Meteorological data
Natural disasters
Physics
ISSN:1742-6588, 1742-6596
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Abstract 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.
AbstractList 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.
Author Anwar, M T
Wibisono, S
Supriyanto, A
Amin, I H A
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StartPage 12077
SubjectTerms Algorithms
Anomalies
Clustering
Data points
Machine learning
Meteorological data
Natural disasters
Physics
Title Multivariate weather anomaly detection using DBSCAN clustering algorithm
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