Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires

Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires

Natural disasters are mostly seasonal and caused by anthropological, climatic, and geological factors that impact human life, economy, ecology, and natural resources. This paper focuses on increasingly widespread forest fires, causing greater destruction in recent years. Data obtained from sensors f...

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Bibliographic Details
Main Authors: Abdul Mutakabbir, Chung-Horng Lung, Kshirasagar Naik, Marzia Zaman, Samuel A. Ajila, Thambirajah Ravichandran, Richard Purcell, Srinivas Sampalli
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
Subjects:
sensors
under-sampling
deep learning
big data analytics
natural fire disasters
climate change
Online Access:https://www.mdpi.com/1424-8220/25/3/792
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