International Journal of Soft Computing

Year: 2021
Volume: 16
Issue: 1
Page No. 1 - 8

Machine Learning Regression Techniques to Predict Burned Area of Forest Fires

Authors : AhmedM. Elshewey

References

Agarwal, S., 2014. Data mining: Data mining concepts and techniques. Proceedings of the 2013 International Conference on Machine Intelligence and Research Advancement, December 21-23, 2013, IEEE, Katra, India, pp: 203-207.

Aghdaei, N., G. Kokogiannakis, D. Daly and T. McCarthy, 2017. Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings. Energy Procedia, 121: 79-86.
CrossRef  |  Direct Link  |  

Al-Janabi, S., I. Al-Shourbaji and M.A. Salman, 2018. Assessing the suitability of soft computing approaches for forest fires prediction. Applied Compu. Inf., 14: 214-224.
CrossRef  |  Direct Link  |  

Aleksandar, P., P. Silvana and Z.P. Valentina, 2015. Multiple linear regression model for predicting bidding price. Tech. Technol. Educ. Manage. (TTEM.), Vol. 1,

Barker, T., 2008. The economics of avoiding dangerous climate change. An editorial essay on The Stern Review. Clim. Change, 89: 173-194.
CrossRef  |  Direct Link  |  

Boubeta, M., M.J. Lombardia, W. Gonzalez-Manteiga and M.F. Marey-Perez, 2016. Burned area prediction with semiparametric models. Int. J. Wildland Fire, 25: 669-678.
CrossRef  |  Direct Link  |  

Calzada, V.R.V., N. Faivre, C.C.F.M. Rego, M.J.M. Rodriguez and G. Xanthopoulos, 2018. Forest fires: Sparking firesmart policies in the EU. European Commission, Belgium.

Castelli, M., L. Vanneschi and A. Popovic, 2015. Predicting burned areas of forest fires: An artificial intelligence approach. Fire Ecol., 11: 106-118.
CrossRef  |  Direct Link  |  

Coffield, S.R., C.A. Graff, Y. Chen, P. Smyth, E. Foufoula-Georgiou and J.T. Randerson, 2019. Machine learning to predict final fire size at the time of ignition. Int. J. Wildland Fire, 28: 861-873.
Direct Link  |  

Cortez, P. and A.D.J.R. Morais, 2007. A data mining approach to predict forest fires using meteorological data. Proceedings of the 13th Portuguese Conference on Artificial Intelligence (EPIA’2007), December 2007, APPIA, Guimaraes, Portugal, pp: 512-523.

Dacre, H.F., B.R. Crawford, A.J. Charlton-Perez, G. Lopez-Saldana, G.H. Griffiths and J.V. Veloso, 2018. Chilean wildfires: Probabilistic prediction, emergency response and public communication. Bull. Am. Meteorol. Soc., 99: 2259-2274.
CrossRef  |  Direct Link  |  

Deng, L., M. Perkowski and J. Saltenberger, 2016. A novel forest fire prediction tool utilizing fire weather and machine learning methods. Proceedings for the 5th International Fire Behavior and Fuels Conference, April 11-15, 2016, International Association of Wildland Fire, Portland, Oregon, USA., pp: 1-6.

Garrard, P., V. Rentoumi, B. Gesierich, B. Miller and M.L. Gorno-Tempini, 2013. Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse. Cortex, 55: 122-129.
CrossRef  |  Direct Link  |  

James, G., D. Witten, T. Hastie and R. Tibshirani, 2013. An Introduction to Statistical Learning. Vol. 6, Springer, New York, USA., ISBN: 978-1-4614-7138-7,.

Kansal, A., Y. Singh, N. Kumar and V. Mohindru, 2015. Detection of forest fires using machine learning technique: A perspective. Proceedings of the 2015 3rd International Conference on Image Information Processing (ICIIP), December 21-24, 2015, IEEE, Waknaghat, India, pp: 241-245.

Laszlo, F. and K. Rajmund, 2016. Characteristics of forest fires and their impact on the environment. AARMS-Acad. Applied Res. Mil. Sci., 15: 5-17.
Direct Link  |  

Lin, Z., H.H. Liu and M. Wotton, 2018. Kalman filter-based large-scale wildfire monitoring with a system of UAVs. IEEE. Trans. Ind. Electron., 66: 606-615.
CrossRef  |  Direct Link  |  

Marandi, A.K. and D.A. Khan, 2015. An impact of linear regression models for improving the software quality with estimated cost. Procedia Comput. Sci., 54: 335-342.
CrossRef  |  Direct Link  |  

Mote, T., A. Singh, M. Prasad and P. Kalwar, 2017. Predicting burned areas of forest fires: An artificial intelligence approach. Int. J. Tech. Res. Appl., 1: 56-58.
Direct Link  |  

Ozbayoglu, A.M. and R. Bozer, 2012. Estimation of the burned area in forest fires using computational intelligence techniques. Procedia Comput. Sci., 12: 282-287.
CrossRef  |  Direct Link  |  

Pereira, J.M., M. Basto, A.F. da Silva, 2016. The logistic lasso and ridge regression in predicting corporate failure. Procedia Econ. Finance, 39: 634-641.
CrossRef  |  Direct Link  |  

Perez-Sanchez, J., P. Jimeno-Saez, J. Senent-Aparicio, J.M. Diaz-Palmero and J.D.D. Cabezas-Cerezo, 2019. Evolution of burned area in forest fires under climate change conditions in Southern Spain using ANN. Applied Sci., Vol. 9, No. 19. 10.3390/app9194155

Radke, D., A. Hessler and D. Ellsworth, 2019. Firecast: Leveraging deep learning to predict wildfire spread. Proceedings of the 28th International Joint Conference on Artificial Intelligence, August 10-16, 2019, AAAI Press, Macao, China, pp: 4575-4581.

Sakr, G.E., I.H. Elhajj, G. Mitri and U.C. Wejinya, 2010. Artificial intelligence for forest fire prediction. Proceedings of the 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, July 6-9, 2010, IEEE, Montreal, Canada, pp: 1311-1316.

Salis, M., B. Arca, F. Alcasena, M. Arianoutsou and V. Bacciu et al., 2016. Predicting wildfire spread and behaviour in Mediterranean landscapes. Int. J. Wildland Fire, 25: 1015-1032.
CrossRef  |  Direct Link  |  

Shidik, G.F. and K. Mustofa, 2014. Predicting size of forest fire using hybrid model. Proceedings of the Information and Communication Technology-EurAsia Conference, April 14-17, 2014, Springer, Berlin, Germany, pp: 316-327.

Singh, B.K., K. Verma and A.S. Thoke, 2015. Investigations on impact of feature normalization techniques on classifier’s performance in breast tumor classification. Int. J. Comput. Appl., 116: 11-15.

Stephens, S.L., B.M. Collins, C.J. Fettig, M.A. Finney and C.M. Hoffman et al., 2017. Drought, tree mortality and wildfire in forests adapted to frequent fire. BioScience, 68: 77-88.
CrossRef  |  Direct Link  |  

Stojanova, D., P. Panov, A. Kobler, S. Dzeroski and K. Taskova, 2006. Learning to predict forest fires with different data mining techniques. Proceedings of the Conference on Data Mining and Data Warehouses, October 9, 2006, Ljubljana, Slovenia, pp: 255-258.

Tedim, F., V. Leone, M. Amraoui, C. Bouillon and M.R. Coughlan et al., 2018. Defining extreme wildfire events: Difficulties, challenges and impacts. Fire, Vol. 1, No. 1. 10.3390/fire1010009

Zhu, H., D. Gao and S. Zhang, 2019. A perceptron algorithm for forest fire prediction based on wireless sensor networks. JIOT., 1: 25-31.

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved