Predicting the Duration of Forest Fires Using Machine Learning Methods
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Narayan Sastry, 2002. "Forest fires, air pollution, and mortality in Southeast Asia," Demography, Springer;Population Association of America (PAA), vol. 39(1), pages 1-23, February.
- Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
- Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
- Dexen D. Z. Xi & Charmaine B. Dean & Stephen W. Taylor, 2021. "Modeling the duration and size of wildfires using joint mixture models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
- Elizabeth Frankenberg & Douglas McKee & Duncan Thomas, 2005. "Health consequences of forest fires in Indonesia," Demography, Springer;Population Association of America (PAA), vol. 42(1), pages 109-129, February.
- M. Flannigan & B. Amiro & K. Logan & B. Stocks & B. Wotton, 2006. "Forest Fires and Climate Change in the 21 ST Century," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 11(4), pages 847-859, July.
- Mubarak A. I. Mahmoud & Honge Ren, 2018. "Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, October.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Rashesh Shrestha, 2019. "Early Life Exposure to Air Pollution, Cognitive Development, and Labor Market Outcome," Asian Economic Papers, MIT Press, vol. 18(2), pages 77-95, Summer.
- Hassani Youssouf & Catherine Liousse & Laurent Roblou & Eric-Michel Assamoi & Raimo O. Salonen & Cara Maesano & Soutrik Banerjee & Isabella Annesi-Maesano, 2014. "Non-Accidental Health Impacts of Wildfire Smoke," IJERPH, MDPI, vol. 11(11), pages 1-33, November.
- Kochi, Ikuho & Champ, Patricia A. & Loomis, John B. & Donovan, Geoffrey H., 2012. "Valuing mortality impacts of smoke exposure from major southern California wildfires," Journal of Forest Economics, Elsevier, vol. 18(1), pages 61-75.
- Nobre, André M. & Karthik, Shravan & Liu, Haohui & Yang, Dazhi & Martins, Fernando R. & Pereira, Enio B. & Rüther, Ricardo & Reindl, Thomas & Peters, Ian Marius, 2016. "On the impact of haze on the yield of photovoltaic systems in Singapore," Renewable Energy, Elsevier, vol. 89(C), pages 389-400.
- Kang Hao Cheong & Nicholas Jinghao Ngiam & Geoffrey G. Morgan & Pin Pin Pek & Benjamin Yong-Qiang Tan & Joel Weijia Lai & Jin Ming Koh & Marcus Eng Hock Ong & Andrew Fu Wah Ho, 2019. "Acute Health Impacts of the Southeast Asian Transboundary Haze Problem—A Review," IJERPH, MDPI, vol. 16(18), pages 1-18, September.
- Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
- Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
- Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
- Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
- Jie Yan & Ruiliang Wang, 2024. "Green Fiscal and Tax Policies in China: An Environmental Dynamic Stochastic General Equilibrium Approach," Sustainability, MDPI, vol. 16(9), pages 1-24, April.
- Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
- Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
- Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
- Cai Chen & Yingli Zhang & Yun Bai & Wenrui Li, 2021. "The impact of green credit on economic growth—The mediating effect of environment on labor supply," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
- Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
- Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
- Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
- Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
- Greg Tkacz & Carolyn A. Wilkins, 2006. "Linear and Threshold Forecasts of Output and Inflation with Stock and Housing Prices," Staff Working Papers 06-25, Bank of Canada.
- Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
More about this item
Keywords
forest fires; machine learning; neural networks; decision trees;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:396-:d:1508318. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.