Articles citing this paper
Building Rothermel fire behaviour fuel models by genetic algorithm optimisation
Davide Ascoli A B , Giorgio Vacchiano A , Renzo Motta A and Giovanni Bovio A
+ Author Affiliations
- Author Affiliations
A Department of Agricultural, Forest and Food Sciences, University of Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Torino, Italy.
B Corresponding author. Email: d.ascoli@unito.it
International Journal of Wildland Fire 24(3) 317-328 https://doi.org/10.1071/WF14097
Submitted: 2 June 2014 Accepted: 16 November 2014 Published: 13 April 2015
21 articles found in Crossref database.
Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire behaviour
Gale Matthew G.,
Cary Geoffrey J.,
Van Dijk Albert I.J.M., Yebra Marta
Remote Sensing of Environment. 2021 255 p.112282
Examining and Reforming the Rothermel Surface Fire Spread Model under No-Wind and Zero-Slope Conditions for the Karst Ecosystems
Zhang Yunlin, Tian Lingling
Forests. 2023 14(6). p.1088
Modeling Wildfire Spread with an Irregular Graph Network
Jiang Wenyu,
Wang Fei,
Su Guofeng,
Li Xin,
Wang Guanning,
Zheng Xinxin,
Wang Ting, Meng Qingxiang
Fire. 2022 5(6). p.185
Higher potential fire intensity at the dry range margins of European mountain trees
Fréjaville Thibaut,
Curt Thomas, Carcaillet Christopher
Journal of Biogeography. 2018 45(9). p.2003
Developing customized fuel models for shrub and bracken communities in Galicia (NW Spain)
Vega José A.,
Álvarez-González Juan Gabriel,
Arellano-Pérez Stéfano,
Fernández Cristina,
Cuiñas Pedro,
Jiménez Enrique,
Fernández-Alonso José M.,
Fontúrbel Teresa,
Alonso-Rego Cecilia, Ruiz-González Ana Daría
Journal of Environmental Management. 2024 351 p.119831
A review of forest visualization and forest fire simulation technology research
Yongjian Huai,
Qingkuo Meng,
Tianrong Ma,
Haifeng Xu,
Xi Zhao,
Mingzhi Cheng, Xinyuan Huang
Journal of Image and Graphics. 2023 28(6). p.1891
Development of Comprehensive Fuel Management Strategies for Reducing Wildfire Risk in Greece
Palaiologou Palaiologos,
Kalabokidis Kostas,
Ager Alan A., Day Michelle A.
Forests. 2020 11(8). p.789
Faster prediction of wildfire behaviour by physical models through application of proper orthogonal decomposition
Guelpa Elisa,
Sciacovelli Adriano,
Verda Vittorio, Ascoli Davide
International Journal of Wildland Fire. 2016 25(11). p.1181
Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data
D’Este Marina,
Elia Mario,
Giannico Vincenzo,
Spano Giuseppina,
Lafortezza Raffaele, Sanesi Giovanni
Remote Sensing. 2021 13(9). p.1658
Using the Rothermel Package in R to Test Standard and Custom Fuel Models against Global Fire Behavior Data
The Third International Conference on Fire Behavior and Risk (2022)
Evidence for lack of a fuel effect on forest and shrubland fire rates of spread under elevated fire danger conditions: implications for modelling and management
Cruz Miguel G.,
Alexander Martin E., Fernandes Paulo M.
International Journal of Wildland Fire. 2022 31(5). p.471
Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey
Giannakidou Sofia,
Radoglou-Grammatikis Panagiotis,
Lagkas Thomas,
Argyriou Vasileios,
Goudos Sotirios,
Markakis Evangelos K., Sarigiannidis Panagiotis
Internet of Things. 2024 26 p.101171
WFNet: A hierarchical convolutional neural network for wildfire spread prediction
Jiang Wenyu,
Qiao Yuming,
Su Guofeng,
Li Xin,
Meng Qingxiang,
Wu Hongying,
Quan Wei,
Wang Jing, Wang Fei
Environmental Modelling & Software. 2023 170 p.105841
Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management
Ascoli D,
Vacchiano G,
Scarpa C,
Arca B,
Barbati A,
Battipaglia G,
Elia M,
Esposito A,
Garfì V,
Lovreglio R,
Mairota P,
Marchetti M,
Marchi E,
Meytre S,
Ottaviano M,
Pellizzaro G,
Rizzolo R,
Sallustio L,
Salis M,
Sirca C,
Valese E,
Ventura A, Bacciu V
iForest - Biogeosciences and Forestry. 2020 13(6). p.513
Performance of operational fire spread models in California
Cardil Adrián,
Monedero Santiago,
SeLegue Phillip,
Navarrete Miguel Ángel,
de-Miguel Sergio,
Purdy Scott,
Marshall Geoff,
Chavez Tim,
Allison Kristen,
Quilez Raúl,
Ortega Macarena,
Silva Carlos A., Ramirez Joaquin
International Journal of Wildland Fire. 2023 32(11). p.1492
Stochastic decision trigger modelling to assess the probability of wildland fire impact
Ramirez J.,
Monedero S.,
Silva C.A., Cardil A.
Science of The Total Environment. 2019 694 p.133505
A review of machine learning applications in wildfire science and management
Jain Piyush,
Coogan Sean C.P.,
Subramanian Sriram Ganapathi,
Crowley Mark,
Taylor Steve, Flannigan Mike D.
Environmental Reviews. 2020 28(4). p.478
Integrating Wildfires Propagation Prediction Into Early Warning of Electrical Transmission Line Outages
Dian Songyi,
Cheng Peng,
Ye Qiang,
Wu Jirong,
Luo Ruisen,
Wang Chen,
Hui Dafeng,
Zhou Ning,
Zou Dong,
Yu Qin, Gong Xiaofeng
IEEE Access. 2019 7 p.27586
Fire Science (2021)
Fire spread predictions: Sweeping uncertainty under the rug
Benali Akli,
Sá Ana C.L.,
Ervilha Ana R.,
Trigo Ricardo M.,
Fernandes Paulo M., Pereira José M.C.
Science of The Total Environment. 2017 592 p.187
Combustion machine learning: Principles, progress and prospects
Ihme Matthias,
Chung Wai Tong, Mishra Aashwin Ananda
Progress in Energy and Combustion Science. 2022 91 p.101010
