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Data and Knowledge Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima and Thailand

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1 Papers

Forest Fire Area Estimation using Support Vector Machine as an Approximator

Topics: Knowledge and Skill Acquistion; Applications: Image and Signal Processing, Pattern Recognition, Decision Making, Human-Robot Interaction, Human Computer Interaction, Cognitive Robotics and Developmental Robotic, Sensor Mesh, Intelligent Networks, Internet Modeling, Multi-sensor Data Fus

Authors: Nittaya Kerdprasop ¹ , Pumrapee Poomka ¹ , Paradee Chuaybamroong ² and Kittisak Kerdprasop ¹

Affiliations: ¹ Data and Knowledge Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima and Thailand , ² Department of Environmental Science, Thammasat University and Thailand

Digital Object Identifier: 10.5220/0007224802690273

Proceedings of IJCCI 2018/IJCCI, ISBN: 978-989-758-327-8

Quick Abstract

Forest fire is critical environmental issue that can cause severe damage. Fast detection and accurate estimation of forest fire burned area can help firefighters to effectively control damage. Thus, the purpose of this paper is to apply state of the art data modeling method to estimate the area of forest fire burning using support vector machine (SVM) algorithm as a tool for area approximation. The dataset is real forest fires data from the Montesinho natural park in the northeast region of Portugal. The original dataset comprises of 517 records with 13 attributes. We randomly sample the data 10 times to obtain 10 data-subsets for building estimation models using two kinds of SVM kernel: radial basis function and polynomial function. The obtained models are compared against other proposed techniques to assess performances based on the two measurement metrics: mean absolute error (MAE) and root mean square error (RMSE). The experimental results show that our SVM predictor using polynomial kernel function can precisely estimate forest fire damage area with the MAE and RMSE as low as 6.48 and 7.65, respectively. These errors are less than other techniques reported in the literature.