Wildfire Predictions: Determining Reliable Models using Fused Dataset

Hariharan Naganathan; Sudarshan P Seshasayee; Jonghoon Kim; Jui-Sheng Chou; Wai K Chong

Wildfire Predictions: Determining Reliable Models using Fused Dataset

Hariharan Naganathan * §

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Sudarshan P Seshasayee

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Jonghoon Kim

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Jui-Sheng Chou

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Wai K Chong

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Arizona State University

Wildfire Predictions: Determining Reliable Models using Fused Dataset

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Abstract

Wildfires are a major environmental hazard that causes fatalities greater than structural fire and other disasters. Computerized models have increased the possibilities of predictions that enhanced the firefighting capabilities in U.S. While predictive models are faster and accurate, it is still important to identify the right model for the data type analyzed. The paper aims at understanding the reliability of three predictive methods using fused dataset. Performances of these methods (Support Vector Machine, K-Nearest Neighbors, and decision tree models) are evaluated using binary and multiclass classifications that predict wildfire occurrence and its severity. Data extracted from meteorological database, and U.S fire database are utilized to understand the accuracy of these models that enhances the discussion on using right model for dataset based on their size. The findings of the paper include SVM as the best optimum models for binary and multiclass classifications on the selected fused dataset.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Hariharan Naganathan, Sudarshan P Seshasayee, Jonghoon Kim, Jui-Sheng Chou, Wai K Chong. 2016. "Wildfire Predictions: Determining Reliable Models using Fused Dataset". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C4).

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GJCST Volume 16 Issue C4

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-C Classification H.2.8

Submission ReceivedDecember 14, 2015
Peer Review Double Blind
Handling Editor
Accepted December 31, 2015
Published January 15, 2016

Version of record

v1.2

Issue date

November 6, 2016

Language

English

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Article Matrices

Total Score: 175

Country: United States

Subject: Global Journal of Computer Science and Technology

Authors: Hariharan Naganathan, Sudarshan P Seshasayee, Jonghoon Kim, Wai K Chong, Jui-Sheng Chou (PhD/Dr. count: 0)

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