Recognition of Forest Fire Spruce Type Tagging using Machine Learning Classification
M. Shyamala Devi1, Shefali Dewangan2, Satwat Kumar Ambashta3, Anjali Jaiswal4, Sairam Kondapalli5
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.
5Sairam Kondapalli, 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.
Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 4309-4313 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5176098319/2019©BEIESP | DOI: 10.35940/ijrte.C5176.098319
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© 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 recent times, the natural resources are demolished due to the technological growth. The agricultural and the forest area are transformed to industries, Storage Warehouse and Container logistics companies to facilitate the living standards. This leads to scarcity of natural resources for the people to live a comfortable life. Due to the change of natural environment and fluctuations in the climate conditions, the forest has the chance of occurrence of fire. The forest fire is the resultant of high temperature, land mine, flight crashes and satellite damages from the environment. The precaution must be taken in advance to protect the coverage of fire. The less attention to fire control may lead to entire damage of the forest and the spreading of fire occurs due to the high wind blow. This makes researchers to focus on helping the forest area to overcome from the fire attack. The detection of fire type is a challenging task after the occurrence of the damage. With this view, we address the prediction of fire type classification using machine learning classification algorithms. The Forest Cover Type dataset is downloaded from UCI Data warehouse repository and done with classification analysis. The prediction of absent hours is achieved in the methodology of four steps. At first, the important feature attributes are found and depicted as a chart. Secondly, the raw dataset is applied to all the classification models like Logistic regression, Kernel SVM, KNN, Decision Tree, Naïve Bayes and Random Forest. Thirdly, the dataset is reduced with PCA and then the reduced dataset is fitted to all the classifiers. Fourth, Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Recall and Precision. The real time execution is performed by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the Random Forest classifier is obtained with the accuracy of 92% before applying PCA. After applying PCA, the classifier namely Random forest is analyzed to be having the accuracy of 78% for 15 components, 83% for 20 components and 89% for 25 components.
Index Terms: Machine Learning, Precision, Recall, FScore and Accuracy.
Scope of the Article: Classification