Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire
Laxmi Kant Sharma
A , Rajit Gupta A * and Naureen Fatima A
+ Author Affiliations
- Author Affiliations
A Remote Sensing & GIS Lab, Department of Environmental Science, School of Earth Sciences, Central University of Rajasthan, N.H.-8, Bandarsindri-305817, Ajmer, Rajasthan, India.
* Correspondence to: 2017phdes03@curaj.ac.in
International Journal of Wildland Fire 31(8) 735-758 https://doi.org/10.1071/WF22016
Submitted: 21 February 2022 Accepted: 30 May 2022 Published: 20 July 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Increasing numbers and intensity of forest fires indicate that forests have become susceptible to fires in the tropics. We assessed the susceptibility of forests to fire in India by comparing six machine learning (ML) algorithms. We identified the best-suited ML algorithms for triggering a fire prediction model, using minimal parameters related to forests, climate and topography. Specifically, we used Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspots from 2001 to 2020 as training data. The Area Under the Receiver Operating Characteristics Curve (ROC/AUC) for the prediction rate showed that the Support Vector Machine (SVM) (ROC/AUC = 0.908) and Artificial Neural Network (ANN) (ROC/AUC = 0.903) show excellent performance. By and large, our results showed that north-east and central India and the lower Himalayan regions were highly susceptible to forest fires. Importantly, the significance of this study lies in the fact that it is possibly among the first to predict forest fire susceptibility in the Indian context, using an integrated approach comprising ML, Google Earth Engine (GEE) and Climate Engine (CE).
Keywords: artificial neural networks, boosted logistic regression, classification and regression trees, forest fire, k-nearest neighbours, machine learning, MODIS, support vector machine, susceptibility mapping.
References
Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015 Scientific Data 5, 170191| TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015Crossref | GoogleScholarGoogle Scholar |
Achu AL, Thomas J, Aju CD, Gopinath G, Kumar S, Reghunath R (2021) Machine-learning modelling of fire susceptibility in a forest–agriculture mosaic landscape of southern India. Ecological Informatics 64, 101348
| Machine-learning modelling of fire susceptibility in a forest–agriculture mosaic landscape of southern India.Crossref | GoogleScholarGoogle Scholar |
Adab H (2017) Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data. Natural Hazards 87, 1807–1825.
| Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data.Crossref | GoogleScholarGoogle Scholar |
Armenteras D, Dávalos LM, Barreto JS, Miranda A, Hernández-Moreno A, Zamorano-Elgueta C, González-Delgado TM, Meza-Elizalde MC, Retana J (2021) Fire-induced loss of the world’s most biodiverse forests in Latin America. Science Advances 7, eabd3357
| Fire-induced loss of the world’s most biodiverse forests in Latin America.Crossref | GoogleScholarGoogle Scholar |
Artés T, Oom D, De Rigo D, Durrant TH, Maianti P, Libertà G, San-Miguel-Ayanz J (2019) A global wildfire dataset for the analysis of fire regimes and fire behaviour. Scientific Data 6, 296
| A global wildfire dataset for the analysis of fire regimes and fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Azad S, Rajeevan M (2016) Possible shift in the ENSO–Indian monsoon rainfall relationship under future global warming. Scientific Reports 6, 20145
| Possible shift in the ENSO–Indian monsoon rainfall relationship under future global warming.Crossref | GoogleScholarGoogle Scholar |
Baccini A, Walker W, Carvalho L, Farina M, Sulla-Menashe D, Houghton RA (2017) Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234.
| Tropical forests are a net carbon source based on aboveground measurements of gain and loss.Crossref | GoogleScholarGoogle Scholar |
Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin Case Study, Italy. Mathematical Geosciences 44, 47–70.
| Support vector machines for landslide susceptibility mapping: the Staffora River Basin Case Study, Italy.Crossref | GoogleScholarGoogle Scholar |
Banerjee P (2021) Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya. Scandinavian Journal of Forest Research 36, 275–288.
| Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya.Crossref | GoogleScholarGoogle Scholar |
Bianco MJ, Gerstoft P, Traer J, Ozanich E, Roch MA, Gannot S, Deledalle CA (2019) Machine learning in acoustics: Theory and applications. Journal of the Acoustical Society of America 146, 3590–3628.
| Machine learning in acoustics: Theory and applications.Crossref | GoogleScholarGoogle Scholar |
Bisquert M, Caselles E, Sánchez JM, Caselles V (2012) Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data. International Journal of Wildland Fire 21, 1025–1029
| Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data.Crossref | GoogleScholarGoogle Scholar |
Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449.
| Forests and climate change: forcings, feedbacks, and the climate benefits of forests.Crossref | GoogleScholarGoogle Scholar |
Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145–1159.
| The use of the area under the ROC curve in the evaluation of machine learning algorithms.Crossref | GoogleScholarGoogle Scholar |
Brando PM, Paolucci L, Ummenhofer CC, Ordway EM, Hartmann H, Cattau ME, Rattis L, Medjibe V, Coe MT, Balch J (2019) Droughts, wildfires, and forest carbon cycling: A pantropical synthesis. Annual Review of Earth and Planetary Sciences 47, 555–581.
| Droughts, wildfires, and forest carbon cycling: A pantropical synthesis.Crossref | GoogleScholarGoogle Scholar |
Breiman L, Friedman JH, Olshen RA, Stone CJ (2017) ‘Classification and regression trees.’ (Routledge)
| Crossref |
Brown EK, Wang J, Feng Y (2021) US wildfire potential: A historical view and future projection using high-resolution climate data. Environmental Research Letters 16, 034060
| US wildfire potential: A historical view and future projection using high-resolution climate data.Crossref | GoogleScholarGoogle Scholar |
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis. International Congress on Environmental Modelling and Software 226, Available at https://scholarsarchive.byu.edu/iemssconference/2012/Stream-B/226
Bui DT, Le K-TT, Nguyen VC, Le HD, Revhaug I (2016) Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression. Remote Sensing 8, 347
| Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression.Crossref | GoogleScholarGoogle Scholar |
Bui DT, Bui QT, Nguyen QP, Pradhan B, Nampak H, Trinh PT (2017) A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology 233, 32–44.
| A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area.Crossref | GoogleScholarGoogle Scholar |
Bui DT, Hoang N-D, Samui P (2019) Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam). Journal of Environmental Management 237, 476–487.
| Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam).Crossref | GoogleScholarGoogle Scholar |
Buma B (2015) Disturbance interactions: Characterization, prediction, and the potential for cascading effects. Ecosphere 6, 1–15.
| Disturbance interactions: Characterization, prediction, and the potential for cascading effects.Crossref | GoogleScholarGoogle Scholar |
Burton C, Betts RA, Jones CD, Feldpausch TR, Cardoso M, Anderson LO (2020) El Niño driven changes in global fire 2015/16. Frontiers in Earth Science 8, 199
| El Niño driven changes in global fire 2015/16.Crossref | GoogleScholarGoogle Scholar |
Cai W, Wang G, Santoso A, Lin X, Wu L (2017) Definition of extreme El Niño and its impact on projected increase in extreme El Niño frequency. Geophysical Research Letters 44, 11–184.
| Definition of extreme El Niño and its impact on projected increase in extreme El Niño frequency.Crossref | GoogleScholarGoogle Scholar |
Champion HG, Seth SK (1968) ‘A revised survey of the forest types of India.’ (Manager of Publications, Government of India)
Chen Y, Randerson JT, Morton DC, DeFries RS, Collatz GJ, Kasibhatla PS, Giglio L, Jin Y, Marlier ME (2011) Forecasting fire season severity in South America using sea surface temperature anomalies. Science 334, 787–791.
| Forecasting fire season severity in South America using sea surface temperature anomalies.Crossref | GoogleScholarGoogle Scholar |
Chirici G, Mura M, McInerney D, Py N, Tomppo EO, Waser LT, Travaglini D, McRoberts RE (2016) A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data. Remote Sensing of Environment 176, 282–294.
| A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data.Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Mouillot F, Van der Werf GR, San Miguel J, Tanase M, Koutsias N, García M, Yebra M, Padilla M, Gitas I, Heil A (2019) Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment 225, 45–64.
| Historical background and current developments for mapping burned area from satellite Earth observation.Crossref | GoogleScholarGoogle Scholar |
Coffield SR, Graff CA, Chen Y, Smyth P, Foufoula-Georgiou E, Randerson JT (2019) Machine learning to predict final fire size at the time of ignition. International Journal of Wildland Fire 28, 861–873.
| Machine learning to predict final fire size at the time of ignition.Crossref | GoogleScholarGoogle Scholar |
Das S, Dey S, Dash SK, Giuliani G, Solmon F (2015) Dust aerosol feedback on the Indian summer monsoon: sensitivity to absorption property. Journal of Geophysical Research: Atmospheres 120, 9642–9652.
| Dust aerosol feedback on the Indian summer monsoon: sensitivity to absorption property.Crossref | GoogleScholarGoogle Scholar |
de Bem PP, de Carvalho Júnior OA, Matricardi EAT, Guimarães RF, Gomes RAT (2018) Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil’s Federal District. International Journal of Wildland Fire 28, 35–45.
| Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil’s Federal District.Crossref | GoogleScholarGoogle Scholar |
De Faria BL, Brando PM, Macedo MN, Panday PK, Soares-Filho BS, Coe MT (2017) Current and future patterns of fire-induced forest degradation in Amazonia. Environmental Research Letters 12, 095005
| Current and future patterns of fire-induced forest degradation in Amazonia.Crossref | GoogleScholarGoogle Scholar |
De Vasconcelos MP, Silva S, Tome M, Alvim M, Pereira JC (2001) Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric Engineering & Remote Sensing 67, 73–81.
Dubayah RO, Luthcke SB, Sabaka TJ, Nicholas JB, Preaux S, Hofton MA (2021) ‘GEDI L3 Gridded Land Surface Metrics, Version 2.’ (ORNL DAAC: Oak Ridge, Tennessee, USA)
| Crossref |
Dutta R, Das A, Aryal J (2016) Big data integration shows Australian bushfire frequency is increasing significantly. Royal Society Open Science 3, 150241
| Big data integration shows Australian bushfire frequency is increasing significantly.Crossref | GoogleScholarGoogle Scholar |
FAO (2010) ‘Global forest resources assessment 2010: Main report.’ (Food and Agriculture Organization of the United Nations)
FAO (2020) ‘Global Forest Resources Assessment 2020: Main report.’ (Food and Agriculture Organization of the United Nations: Rome)
| Crossref |
French CC (2020) America on fire: climate change, wildfires & insuring natural catastrophes. UC Davis Law Review 54, 817
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics 28, 337–407.
| Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors).Crossref | GoogleScholarGoogle Scholar |
Gannon CS, Steinberg NC (2021) A global assessment of wildfire potential under climate change utilizing Keetch–Byram Drought Index and land cover classifications. Environmental Research Communications 3, 035002
| A global assessment of wildfire potential under climate change utilizing Keetch–Byram Drought Index and land cover classifications.Crossref | GoogleScholarGoogle Scholar |
Gao F, Klein R, Klein B, Lin X, Wahba G, Xiang D (2000) Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV. The Annals of Statistics 28, 1570–1600.
| Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Schroeder W, Hall JV, Justice CO (2018) MODIS Collection 6 Active Fire Product User’s Guide Revision B. Available at http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, 18–27.
| Google Earth Engine: Planetary-scale geospatial analysis for everyone.Crossref | GoogleScholarGoogle Scholar |
Grau J, Grosse I, Keilwagen J (2015) PRROC: Computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics 31, 2595–2597.
| PRROC: Computing and visualizing precision-recall and receiver operating characteristic curves in R.Crossref | GoogleScholarGoogle Scholar |
Guhathakurta P, Rajeevan M (2008) Trends in the rainfall pattern over India. International Journal of Climatology: A Journal of the Royal Meteorological Society 28, 1453–1469.
| Trends in the rainfall pattern over India.Crossref | GoogleScholarGoogle Scholar |
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High‐resolution global maps of 21st‐century forest cover change. Science 342, 850–853.
| High‐resolution global maps of 21st‐century forest cover change.Crossref | GoogleScholarGoogle Scholar |
Harrison SP, Prentice IC, Bloomfield KJ, Dong N, Forkel M, Forrest M, Ningthoujam RK, Pellegrini A, Shen Y, Baudena M, Cardoso AW (2021) Understanding and modelling wildfire regimes: an ecological perspective. Environmental Research Letters 16, 125008
| Understanding and modelling wildfire regimes: an ecological perspective.Crossref | GoogleScholarGoogle Scholar |
Herawati H, Santoso H (2011) Tropical forest susceptibility to and risk of fire under changing climate: A review of fire nature, policy and institutions in Indonesia. Forest Policy and Economics 13, 227–233.
| Tropical forest susceptibility to and risk of fire under changing climate: A review of fire nature, policy and institutions in Indonesia.Crossref | GoogleScholarGoogle Scholar |
Huang S, Tang L, Hupy JP, Wang Y, Shao G (2021) A commentary review on the use of Normalized Difference Vegetation Index (NDVI) in the era of popular remote sensing. Journal of Forestry Research 32, 1–6.
| A commentary review on the use of Normalized Difference Vegetation Index (NDVI) in the era of popular remote sensing.Crossref | GoogleScholarGoogle Scholar |
Huntington JL, Hegewisch KC, Daudert B, Morton CG, Abatzoglou JT, McEvoy DJ, Erickson T (2017) Climate Engine: cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding. Bulletin of the American Meteorological Society 98, 2397–2410.
| Climate Engine: cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding.Crossref | GoogleScholarGoogle Scholar |
ISFR (2021) India State of Forest Report. Forest Survey of India, Ministry of Environment, Forest & Climate Change, Government of India, Dehradun.
Jaafari A, Pourghasemi HR (2019) Factors influencing regional-scale wildfire probability in Iran: an application of random forest and support vector machine. In ‘Spatial modeling in GIS and R for Earth and Environmental sciences’. (Eds HR Pourghasemi, C Gokceoglu). pp. 607–619.
| Crossref |
Jafari Goldarag Y, Mohammadzadeh A, Ardakani AS (2016) Fire risk assessment using neural network and logistic regression. Journal of the Indian Society of Remote Sensing 44, 885–894.
| Fire risk assessment using neural network and logistic regression.Crossref | GoogleScholarGoogle Scholar |
Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020) A review of machine learning applications in wildfire science and management. Environmental Reviews 28, 478–505.
| A review of machine learning applications in wildfire science and management.Crossref | GoogleScholarGoogle Scholar |
Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DM (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications 6, 7537
| Climate-induced variations in global wildfire danger from 1979 to 2013.Crossref | GoogleScholarGoogle Scholar |
Juárez-Orozco SM, Siebe C, Fernández y Fernández D (2017) Causes and effects of forest fires in tropical rainforests: a bibliometric approach. Tropical Conservation Science 10, 1940082917737207
| Causes and effects of forest fires in tropical rainforests: a bibliometric approach.Crossref | GoogleScholarGoogle Scholar |
Kalantar B, Ueda N, Idrees MO, Janizadeh S, Ahmadi K, Shabani F (2020) Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data. Remote Sensing 12, 3682
| Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Kale MP, Ramachandran RM, Pardeshi SN, Chavan M, Joshi PK, Pai DS, et al. (2017) Are climate extremities changing forest fire regimes in India? An analysis using MODIS fire locations during 2003–2013 and gridded climate data of India meteorological department. Proceedings of the National Academy of Sciences, India – Section A: Physical Sciences 87, 827–843.
| Are climate extremities changing forest fire regimes in India? An analysis using MODIS fire locations during 2003–2013 and gridded climate data of India meteorological department.Crossref | GoogleScholarGoogle Scholar |
Kamarudin MH, Maple C, Watson T, Safa NS (2017) A logitboost-based algorithm for detecting known and unknown web attacks. IEEE Access 5, 26190–26200.
| A logitboost-based algorithm for detecting known and unknown web attacks.Crossref | GoogleScholarGoogle Scholar |
Krzywinksi M, Altman N (2017) Correction: Corrigendum: Classification and regression trees. Nature Methods 14, 928
| Correction: Corrigendum: Classification and regression trees.Crossref | GoogleScholarGoogle Scholar |
Kuhn M (2008) Building predictive models in R using the caret package. Journal of Statistical Software 28, 1–26.
| Building predictive models in R using the caret package.Crossref | GoogleScholarGoogle Scholar |
Kumar N, Kumar A (2020) Australian bushfire detection using machine learning and neural networks. In ‘2020 7th International Conference on Smart Structures and Systems (ICSSS)’. pp. 1–7. (IEEE)
| Crossref |
Lang N, Schindler K, Wegner JD (2019) Country-wide high-resolution vegetation height mapping with Sentinel-2. Remote Sensing of Environment 233, 111347
| Country-wide high-resolution vegetation height mapping with Sentinel-2.Crossref | GoogleScholarGoogle Scholar |
Lee S, Park I, Choi JK (2012a) Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environmental Management 49, 347–358.
| Spatial prediction of ground subsidence susceptibility using an artificial neural network.Crossref | GoogleScholarGoogle Scholar |
Lee S, Song KY, Kim Y, Park I (2012b) Cartographie régionale du potentiel de productivité des aquifères à partir d’un système d’information géographique base sur un modèle de réseau de neurones artificiels. Hydrogeology Journal 20, 1511–1527.
| Cartographie régionale du potentiel de productivité des aquifères à partir d’un système d’information géographique base sur un modèle de réseau de neurones artificiels.Crossref | GoogleScholarGoogle Scholar |
Li J, Heap AD, Potter A, Daniell JJ (2011) Application of machine learning methods to spatial interpolation of environmental variables. Environmental Modelling and Software 26, 1647–1659.
| Application of machine learning methods to spatial interpolation of environmental variables.Crossref | GoogleScholarGoogle Scholar |
Lunardon N, Menardi G, Torelli N (2014) ROSE: a package for binary imbalanced learning. The R Journal 6, 79–89.
| ROSE: a package for binary imbalanced learning.Crossref | GoogleScholarGoogle Scholar |
Ma W, Feng Z, Cheng Z, Chen S, Wang F (2020) Identifying forest fire driving factors and related impacts in China using random forest algorithm. Forests 11, 507
| Identifying forest fire driving factors and related impacts in China using random forest algorithm.Crossref | GoogleScholarGoogle Scholar |
Maeda EE, Formaggio AR, Shimabukuro YE, Arcoverde GFB, Hansen MC (2009) Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks. International Journal of Applied Earth Observation and Geoinformation 11, 265–272.
| Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks.Crossref | GoogleScholarGoogle Scholar |
Magnussen S, Tomppo E, McRoberts RE (2010) A model-assisted k-nearest neighbour approach to remove extrapolation bias. Scandinavian Journal of Forest Research 25, 174–184.
| A model-assisted k-nearest neighbour approach to remove extrapolation bias.Crossref | GoogleScholarGoogle Scholar |
Martínez-Austria PF, Bandala ER, Patiño-Gómez C (2016) Temperature and heat wave trends in northwest Mexico. Physics and Chemistry of the Earth, Parts A/B/C 91, 20–26.
| Temperature and heat wave trends in northwest Mexico.Crossref | GoogleScholarGoogle Scholar |
Mayr MJ, Vanselow KA, Samimi C (2018) Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000–2016) on the controls of fire activity in Namibia from spatial predictive models. Ecological Indicators 91, 324–337.
| Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000–2016) on the controls of fire activity in Namibia from spatial predictive models.Crossref | GoogleScholarGoogle Scholar |
Milanović S, Marković N, Pamučar D, Gigović L, Kostić P, Milanović SD (2021) Forest fire probability mapping in eastern Serbia: Logistic regression versus random forest method. Forests 12, 5
| Forest fire probability mapping in eastern Serbia: Logistic regression versus random forest method.Crossref | GoogleScholarGoogle Scholar |
Mohajane M, Costache R, Karimi F, Pham QB, Essahlaoui A, Nguyen H, Laneve G, Oudija F (2021) Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators 129, 107869
| Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area.Crossref | GoogleScholarGoogle Scholar |
Mozny M, Trnka M, Brázdil R (2021) Climate change driven changes of vegetation fires in the Czech Republic. Theoretical and Applied Climatology 143, 691–699.
| Climate change driven changes of vegetation fires in the Czech Republic.Crossref | GoogleScholarGoogle Scholar |
Murtaza G, Shuib L, Abdul Wahab AW, Mujtaba G, Nweke HF, Al-garadi MA, Zulfiqar F, Raza G, Azmi NA (2020) Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artificial Intelligence Review 53, 1655–1720.
| Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges.Crossref | GoogleScholarGoogle Scholar |
Naderpour M, Rizeei HM, Ramezani F (2021) Forest fire risk prediction: a spatial deep neural network-based framework. Remote Sensing 13, 2513
| Forest fire risk prediction: a spatial deep neural network-based framework.Crossref | GoogleScholarGoogle Scholar |
Naghibi SA, Pourghasemi HR, Abbaspour K (2018) A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theoretical and Applied Climatology 131, 967–984.
| A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS.Crossref | GoogleScholarGoogle Scholar |
Navarro G, Caballero I, Silva G, Parra PC, Vázquez Á, Caldeira R (2017) Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation 58, 97–106.
| Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery.Crossref | GoogleScholarGoogle Scholar |
Negara BS, Kurniawan R, Nazri MZA, Abdullah SNHS, Saputra RW, Ismanto A (2020) Riau forest fire prediction using supervised machine learning. Journal of Physics: Conference Series 1566, 012002
| Riau forest fire prediction using supervised machine learning.Crossref | GoogleScholarGoogle Scholar |
Park SY, Liu Y (2011) Robust penalized logistic regression with truncated loss functions. Canadian Journal of Statistics 39, 300–323.
| Robust penalized logistic regression with truncated loss functions.Crossref | GoogleScholarGoogle Scholar |
Pham BT, Pradhan B, Tien Bui D, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling & Software 84, 240–250.
| A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India).Crossref | GoogleScholarGoogle Scholar |
Pham BT, Jaafari A, Avand M, Al-Ansari N, Dinh Du T, Yen HPH, Phong TV, Nguyen DH, Le HV, Mafi-Gholami D, Prakash I (2020) Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry 12, 1022
| Performance evaluation of machine learning methods for forest fire modeling and prediction.Crossref | GoogleScholarGoogle Scholar |
Pourghasemi HR, Beheshtirad M, Pradhan B (2016) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk 7, 861–885.
| A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping.Crossref | GoogleScholarGoogle Scholar |
Pourghasemi HR, Kariminejad N, Amiri M, Edalat M, Zarafshar M, Blaschke T, Cerda A (2020) Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Sci Rep 10, 3203
| Assessing and mapping multi-hazard risk susceptibility using a machine learning technique.Crossref | GoogleScholarGoogle Scholar |
Pourtaghi ZS, Pourghasemi HR, Rossi M (2015) Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environmental Earth Sciences 73, 1515–1533.
| Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran.Crossref | GoogleScholarGoogle Scholar |
Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T (2016) Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators 64, 72–84.
| Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques.Crossref | GoogleScholarGoogle Scholar |
Praveen B, Talukdar S, S, Mahato S, Mondal J, Sharma P, Islam ARMT, Rahman A, et al. (2020) Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Sci Rep 10, 10342–10342.
| Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches.Crossref | GoogleScholarGoogle Scholar |
Rajashekar G, Fararoda R, Reddy RS, Jha CS, Ganeshaiah KN, Singh JS, Dadhwal VK (2018) Spatial distribution of forest biomass carbon (above and below ground) in Indian forests. Ecological Indicators 85, 742–752.
| Spatial distribution of forest biomass carbon (above and below ground) in Indian forests.Crossref | GoogleScholarGoogle Scholar |
Reddy CS, Jha CS, Diwakar PG, Dadhwal VK (2015) Nationwide classification of forest types of India using remote sensing and GIS. Environmental Monitoring and Assessment 187, 777
| Nationwide classification of forest types of India using remote sensing and GIS.Crossref | GoogleScholarGoogle Scholar |
Rodrigues M, De la Riva J (2014) An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environmental Modelling and Software 57, 192–201.
| An insight into machine-learning algorithms to model human-caused wildfire occurrence.Crossref | GoogleScholarGoogle Scholar |
Roteta E, Bastarrika A, Padilla M, Storm T, Chuvieco E (2019) Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa. Remote Sensing of Environment 222, 1–17.
| Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa.Crossref | GoogleScholarGoogle Scholar |
RStudio Team (2021) ‘RStudio: Integrated Development for R.’ (RStudio, PBC: Boston, MA) Available at http://www.rstudio.com/
Sachdeva S, Bhatia T, Verma AK (2018) GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping. Natural Hazards 92, 1399–1418.
| GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping.Crossref | GoogleScholarGoogle Scholar |
Sakr GE, Elhajj IH, Mitri G, Wejinya UC (2010) Artificial intelligence for forest fire prediction. In ‘2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics’, Montreal, QC, Canada. pp. 1311–1316. (IEEE)
| Crossref |
Sannigrahi S, Pilla F, Basu B, Basu AS, Sarkar K, Chakraborti S, Joshi PK, Zhang Q, Wang Y, Bhatt S, Bhatt A (2020) Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches. Science of the Total Environment 725, 138331
| Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches.Crossref | GoogleScholarGoogle Scholar |
Satir O, Berberoglu S, Donmez C (2016) Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk 7, 1645–1658.
| Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem.Crossref | GoogleScholarGoogle Scholar |
Sulova A, Joker Arsanjani J (2021) Exploratory analysis of driving force of wildfires in Australia: An application of machine learning within Google Earth Engine. Remote Sensing 13, 10
| Exploratory analysis of driving force of wildfires in Australia: An application of machine learning within Google Earth Engine.Crossref | GoogleScholarGoogle Scholar |
Swann ALS, Laguë MM, Garcia ES, Field JP, Breshears DD, Moore DJP, Saleska SR, Stark SC, Villegas JC, Law DJ, Minor DM (2018) Continental‐scale consequences of tree die‐offs in North America: Identifying where forest loss matters most. Environmental Research Letters 13, 055014
| Continental‐scale consequences of tree die‐offs in North America: Identifying where forest loss matters most.Crossref | GoogleScholarGoogle Scholar |
Syifa M, Panahi M, Lee CW (2020) Mapping of post-wildfire burned area using a hybrid algorithm and satellite data: the case of the Camp Fire wildfire in California, USA. Remote Sensing 12, 623
| Mapping of post-wildfire burned area using a hybrid algorithm and satellite data: the case of the Camp Fire wildfire in California, USA.Crossref | GoogleScholarGoogle Scholar |
Takeuchi W, Darmawan S, Shofiyati R, Khiem MV, Oo KS, Pimple U, Heng S (2015) Near-real time meteorological drought monitoring and early warning system for croplands in Asia. In ‘Asian Conference on Remote Sensing 2015: Fostering Resilient Growth in Asia’. Vol. 1, pp. 171–178.
USGCRP (2017) Climate Science Special Report: Fourth National Climate Assessment, Volume I. (Eds Wuebbles DJ, Fahey DW, Hibbard KA, Dokken DJ, Stewart C, Maycock TK) (US Global Change Research Program: Washington DC, USA) Available at https://www.nrc.gov/docs/ML1900/ML19008A410.pdf
van Wees D, van Der Werf GR, Randerson JT, Andela N, Chen Y, Morton DC (2021) The role of fire in global forest loss dynamics. Global Change Biology 27, 2377
| The role of fire in global forest loss dynamics.Crossref | GoogleScholarGoogle Scholar |
Vapnik V (1995) ‘The nature of statistical learning theory.’ (Springer Science & Business Media)
| Crossref |
Wahba G (1999) Support vector machines, reproducing kernel hilbert spaces and the randomized GACV. In ‘Advances in Kernel Methods Support Vector Learning’. (Eds S Bernhard, CJS Burges, AJ Smola) pp. 69–88. (MIT Press: Cambridge, MA)
Williams AP, Abatzoglou JT, Gershunov A, Guzman‐Morales J, Bishop DA, Balch JK, Lettenmaier DP (2019) Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Future 7, 892–910.
| Observed impacts of anthropogenic climate change on wildfire in California.Crossref | GoogleScholarGoogle Scholar |
World Bank (2018) ‘Strengthening forest fire management in India.’ (World Bank: Washington DC)
World Bank Group (2021) ‘Climate risk country profile: India.’ (The World Bank Group)
Wu N, Zhang C, Bai X, Du X, He Y (2018) Discrimination of chrysanthemum varieties using hyperspectral imaging combined with a deep Convolutional Neural Network. Molecules 23, 2831
| Discrimination of chrysanthemum varieties using hyperspectral imaging combined with a deep Convolutional Neural Network.Crossref | GoogleScholarGoogle Scholar |
Wu X, Kumar V, Ross QJ, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowledge and Information Systems 14, 1–37.
| Top 10 algorithms in data mining.Crossref | GoogleScholarGoogle Scholar |
Zhang G, Eddy Patuwo B, Hu MY (1998) Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14, 35–62.
| Forecasting with artificial neural networks: The state of the art.Crossref | GoogleScholarGoogle Scholar |
Zhang G, Wang M, Liu K (2019) Forest fire susceptibility modeling using a convolutional neural network for Yunnan Province of China. International Journal of Disaster Risk Science 10, 386–403.
| Forest fire susceptibility modeling using a convolutional neural network for Yunnan Province of China.Crossref | GoogleScholarGoogle Scholar |
Zhang G, Wang M, Liu K (2021) Deep neural networks for global wildfire susceptibility modelling. Ecological Indicators 127, 107735
| Deep neural networks for global wildfire susceptibility modelling.Crossref | GoogleScholarGoogle Scholar |
Zhao Y, Ma J, Li X, Zhang J (2018) Saliency detection and deep learning based wildfire identification in UAV imagery. Sensors 18, 712
| Saliency detection and deep learning based wildfire identification in UAV imagery.Crossref | GoogleScholarGoogle Scholar |
