ESP Journal of Engineering & Technology Advancements |
© 2023 by ESP JETA |
Volume 3 Issue 1 |
Year of Publication : 2023 |
Authors : V. Mohandas, Dr. S.M.H. Sithi Shameen Fathima |
:10.56472/25832646/JETA-V3I1P110 |
V. Mohandas, Dr. S.M.H. Sithi Shameen Fathima, 2023. "Emerging Methods for Early Detection of Forest Fires Using Artificial Intelligence", ESP Journal of Engineering & Technology Advancements 3(1): 57-61.
Forest fires are one of the natural disasters that happen the most frequently today. Forest fires are a matter of concern because they cause extensive damage to environment, property and human life. By doing this, the region’s resources and flora and fauna may both be saved. In particular, we proposed a platform of Artificial Intelligence. The use of computer vision techniques for smoke and fire detection and recognition based on still photos or video input from the cameras. This will enable the video surveillance systems on forest to handle more complex situations in real world. Deep learning method “convolution neural network” can be used for finding the amount of fire. This will enable the video surveillance systems on forest to handle more complex situations in real world. The accuracy is based on the algorithm which we are going to use and the datasets and splitting them into train set and test set.
[1] László F, Rajmund K, “Characteristics of forest fires and their impact on the environment”, Academic and Applied Research in Military and Public Management Science, vol.15, pp.5-17, 2016.
[2] Barker T, “The economics of avoiding dangerous climate change. An editorial essay on The Stern Review”, Climatic Change, vol. 89, pp. 173-194, 2008.
[3] Mote T, Singh A, Prasad M, Kalwar P, “Predicting burned areas of forest fires: an artificial intelligence approach”, International Journal of Technical Research and Applications, vol. 43, pp. 56-58, 2017.
[4] Lin Z, Liu HH, Wotton M, “Kalman filter- based large-scale wildfire monitoring with a system of UAVs”, IEEE Transactions on Industrial Electronics, vol. 66, pp. 606-615, 2018.
[5] Agarwal S, “Data mining: data mining concepts and techniques”, In International Conference on Machine Intelligence and Research Advancement, IEEE, 2013.
[6] Cortez P, Morais AD, “A data mining approach to predict forest fires using meteorological data, Environmental Science, 2007.
[7] Özbayoğlu AM, Bozer R, “Estimation of the burned area in forest fires using computational intelligence techniques”, Procedia Computer Science, vol.12, pp. 282- 287, 2012.
[8] Salis M, Arca B, Alcasena F, Arianoutsou M, Bacciu V, Duce P, Duguay B, Koutsias N, Mallinis G, Mitsopoulos I, Moreno JM, “Predicting wildfire spread and behaviour in Mediterranean landscapes”, International Journal of Wildland Fire, vol. 25, pp. 1015- 1032, 2016.
[9] Castelli M, Vanneschi L, Popovič A, “Predicting burned areas of forest fires: an artificial intelligence approach”, Fire Ecology, vol. 11, pp. 106-118, 2015.
[10] Radke D, Hessler A, Ellsworth D, “Forecast: leveraging deep learning to predict wildfire spread”, In Proceedings of the 28th International Joint Conference on Artificial Intelligence, AAAI Press, 2019.
[11] Kalla, Dinesh and Smith, Nathan and Samaah, Fnu and Polimetla, Kiran, Facial Emotion and Sentiment Detection Using Convolutional Neural Network (January 2021). Indian Journal of Artificial Intelligence Research (INDJAIR), Volume 1, Issue 1, January-December 2021, pp. 1–13, Article ID: INDJAIR_01_01_001, Available at SSRN: https://ssrn.com/abstract=4690960
Artificial intelligence, Machine learning, Forest fires, Algorithms.