Backward Eliminated Formulation of Fire Area Coverage using Machine Learning Regression
M. Shyamala Devi1, Shefali Dewangan2, Satwat Kumar Ambashta3, Anjali Jaiswal4, Nariboyena Vijaya Sai Ram5
1M. Shyamala Devi, Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
2Shefali Dewangan, II Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
3Satwat Kumar Ambastha, II Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
4Anjali Jaiswal, II Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
5Nariboyena Vijaya Sai Ram, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
Manuscript received on September 17, 2019. | Revised Manuscript received on 22 September, 2019. | Manuscript published on October 10, 2019. | PP: 1565-1569 | Volume-8 Issue-12, October 2019. | Retrieval Number: L31301081219/2019©BEIESP | DOI: 10.35940/ijitee.L3130.1081219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s modern world, the environmental wealth is degraded due to the advancement in the technology. The software development leads to the emission of electronic waste that affects the whole part of the country. The forest area and the agricultural land are converted into living places, companies, industries, server warehouse and heavy workstations. Due to this, heavy damages occur in the environmental resources. The basic characteristics of the nature quality are becoming poor due to the technological advancement. Due to the heavy emission of rays in the environment, there is a chance for the occurrence of fire in the forest. This leads to the challenging issue of predicting the area coverage of the fire in the forest. After the event of fire damage, it is a difficult task to analyze the area that suffered from the fire. With this analytical view, this paper focuses on finding the area coverage of fire using various regression algorithms. The forest area coverage dataset from the UCI machine learning repository is used for analyzing the area coverage of the fire. The prediction of area coverage of fire is accomplished in four ways. Firstly, the raw data set is fitted with various regression algorithms to predict the fire area coverage. Secondly, the data set is tailored by the feature selection algorithm namely backward elimination technique. Thirdly, the backward eliminated reduced fire area coverage data set is fitted with various regression algorithms to predict the fire area coverage. Fourth, the performance analysis is done for the raw data set and backward eliminated reduced fire area coverage data set by reviewing the performance metrics mean squared error (MSE), Mean Absolute Error (MAE) and R2 Score. This paper is implemented by python scripts in Anaconda Spyder Navigator. Experimental Result shows that the Passive Aggressive regressor have the effective prediction of fire area coverage with minimum MSE of 0.07, MAE of 1.03 and equitable R2 Score of 0.93 without backward elimination. In the same way, the Passive Aggressive regressor MSE of 0.06, MAE of 1.02 and equitable R2 Score of 0.96 with backward elimination.
Keywords: Machine Learning, Feature Extraction, PCA, MSE, MAE, R2 Score.
Scope of the Article: Machine Learning