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An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

N. Vanitha1,*, C. R. Rene Robin1, D. Doreen Hephzibah Miriam2
1 Sri Sai Ram Engineering College, Chennai, 600044, Tamilnadu, India
2 Director of Computational Intelligence Research Foundation, Chennai, 600023, Tamilnadu, India
* Corresponding Author: N. Vanitha. Email:

Computer Systems Science and Engineering 2023, 44(3), 2323-2336. https://doi.org/10.32604/csse.2023.028309

Received 07 February 2022; Accepted 16 March 2022; Issue published 01 August 2022

Abstract

Abstract: Tropical cyclones (TC) are often associated with severe weather conditions which cause great losses to lives and property. The precise classification of cyclone tracks is significantly important in the field of weather forecasting. In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine (SVM) to classify the tropical cyclone tracks into four types of classes namely straight, quasi-straight, curving and sinuous based on the track shape. Tropical Cyclone TRacks Ontology (TCTRO) described in this paper is a knowledge base which comprises of classes, objects and data properties that represent the interaction among the TC characteristics. A set of SWRL (Semantic Web Rule Language) rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology. Furthermore, we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset. According to experiments on the IBTrACS dataset, the proposed ontology based SVM classifier achieves an accuracy of 98.3% with reduced classification error rates.

Keywords

Tropical cyclones classification; support vector machine; ontology; SWRL reasoning; SVM classification

Cite This Article

N. Vanitha, C. R. R. Robin and D. D. H. Miriam, "An ontology based cyclone tracks classification using swrl reasoning and svm," Computer Systems Science and Engineering, vol. 44, no.3, pp. 2323–2336, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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