Automated Analysis of Australian Tropical Cyclones with Regression, Clustering and Convolutional Neural Network

Fahim Sufi (), Edris Alam and Musleh Alsulami
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Fahim Sufi: Federal Government, Melbourne, VIC 3000, Australia
Edris Alam: Faculty of Resilience, Rabdan Academy, P.O. Box 114646, Abu Dhabi 22401, United Arab Emirates
Musleh Alsulami: Information Systems Department, Umm Al-Qura University (UQU), Makkah 24382, Saudi Arabia

Sustainability, 2022, vol. 14, issue 16, 1-23

Abstract: Tropical cyclones take precious lives, damage critical infrastructure, and cause economic losses worth billions of dollars in Australia. To reduce the detrimental effect of cyclones, a comprehensive understanding of cyclones using artificial intelligence (AI) is crucial. Although event records on Australian tropical cyclones have been documented over the last 4 decades, deep learning studies on these events have not been reported. This paper presents automated AI-based regression, anomaly detection, and clustering techniques on the largest available cyclone repository covering 28,713 records with almost 80 cyclone-related parameters from 17 January 1907 to 11 May 2022. Experimentation with both linear and logistic regression on this dataset resulted in 33 critical insights on factors influencing the central pressure of cyclones. Moreover, automated clustering determined four different clusters highlighting the conditions for low central pressure. Anomaly detection at 70% sensitivity identified 12 anomalies and explained the root causes of these anomalies. This study also projected parameterization and fine-tuning of AI-algorithms at different sensitivity levels. Most importantly, we mathematically evaluated robustness by supporting an enormous scenario space of 4.737 × 10 8234 . A disaster strategist or researcher can use the deployed system in iOS, Android, or Windows platforms to make evidence-based policy decisions on Australian tropical cyclones.

Keywords: Australian tropical cyclones; linear regression; logistic regression; anomaly detection; clustering cyclones; AI based cyclone analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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