Detection of forest fires using machine learning technique: A perspective

• Published in: 2015 Third International Conference on Image Information Processing (ICIIP) pp. 241 - 245
• Main Authors: Kansal, Aditi, Singh, Yashwant, Kumar, Nagesh, Mohindru, Vandana
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2015
• Subjects: Classification; Decision Tree; Regression; Sensor; SVM; Wireless sensor networks
• DOI: 10.1109/ICIIP.2015.7414773

Wireless Sensor Networks (WSN) has gained attention as it has been useful in warning about disasters. Predicting natural disasters like hailstorm, fire, rainfall etc. by WSN are infrequent and stochastic. This is an important topic of research. Detection of these disasters should be fast and accurate as they may cause damage and destruction at a large scale. In this paper, comparison of various machine learning techniques such as SVM, regression, decision trees, neural networks etc. has been done for prediction of forest fires. The proposed approach in this paper presents how regression works best for detection of forest fires with high accuracy by dividing the dataset. Fast detection of forest fires is done in this paper by taking less time as compared to other machine learning techniques.