Research graph
References from Ensemble fire probability prediction by integrating meteorological AI models with lightweight prediction networks. Local targets link to admitted publications; unresolved targets remain external evidence.
Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model
10.1016/j.scitotenv.2023.163004 · 2023 · External reference
Explainable AI-driven wildfire prediction in Australia: SHAP and feature importance to identify environmental drivers in the age of climate change
10.3390/fire8110421 · 2025 · External reference
Enhancing prediction of wildfire occurrence and behavior in alaska using spatio-temporal clustering and ensemble machine learning
10.1016/j.ecoinf.2024.102963 · 2025 · External reference
21St century drought-related fires counteract the decline of amazon deforestation carbon emissions
10.1038/s41467-017-02771-y · 2018 · External reference
Predicting wildfire burns from big geodata using deep learning
10.1016/j.ssci.2021.105276 · 2021 · External reference
Unresolved reference
2022 · External reference
Large-scale machine learning with stochastic gradient descent
2010 · External reference
Human exposure and sensitivity to globally extreme wildfire events
10.1038/s41559-016-0058 · 2017 · External reference
Predicting wildfire occurrences in Portugal using machine learning classification models
10.1016/j.ecoinf.2025.103455 · 2025 · External reference
Xgboost: A scalable tree boosting system
2016 · External reference
Unresolved reference
2023 · External reference
Breaking the computational barrier in high-resolution weather forecasting with decoupled training
10.1016/j.aosl.2026.100821 · 2026 · External reference
Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast
10.1038/s41612-023-00512-1 · 2023 · External reference
Global data-driven prediction of fire activity
10.1038/s41467-025-58097-7 · 2025 · External reference
The potential predictability of fire danger provided by numerical weather prediction
10.1175/jamc-d-15-0297.1 · 2016 · External reference
Estimation of fire counts and fire radiative power using satellite optical and microwave vegetation indices with random forest method
10.1029/2024jd041680 · 2025 · External reference
A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment
10.1016/j.ecoinf.2025.103325 · 2025 · External reference
Finding structure in time
10.1207/s15516709cog1402_1 · 1990 · External reference
VIIRS night-time lights
10.1080/01431161.2017.1342050 · 2017 · External reference
Forest fire probability zonation using dNBR and machine learning models: a case study at the similipal biosphere reserve (SBR), odisha, India
2025 · External reference
Deep learning modeling of human activity affected wildfire risk by incorporating structural features: A case study in eastern China
10.1016/j.ecolind.2024.111946 · 2024 · External reference
Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections
10.1016/j.wace.2025.100751 · 2025 · External reference
The ERA5 global reanalysis
10.1002/qj.3803 · 2020 · External reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · 1997 · External reference
Unresolved reference
2013 · External reference
Generation of 30 m resolution monthly burned area product in africa based on landsat 8/9 and sentinel-2 data
10.1016/j.isprsjprs.2025.09.012 · 2025 · External reference
Next day wildfire spread: A machine learning dataset to predict wildfire spreading from remote-sensing data
10.1109/tgrs.2022.3192974 · 2022 · External reference
LandScan global 30 arcsecond annual global gridded population datasets from 2000 to 2022
10.1038/s41597-025-04817-z · 2025 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions
10.1016/j.scitotenv.2018.03.290 · 2018 · External reference
Fires dynamics in the pantanal: Impacts of anthropogenic activities and climate change
10.1016/j.jenvman.2021.113586 · 2021 · External reference
FLDAS noah land surface model L4 global monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS)(fldas_noah01_c_gl_m)
2018 · External reference
FireCastNet: earth-as-a-graph for seasonal fire prediction
10.1038/s41598-025-30645-7 · 2025 · External reference
Advancing wildfire susceptibility mapping through maching learning and shapley additive explanations-integrated geospatial analysis in northern Morocco’s mediterranean region
10.3389/ffgc.2025.1705341 · 2025 · External reference
Unresolved reference
External reference
Spatio-temporal distribution of fire activity in protected areas of sub-saharan africa derived from MODIS data
10.1016/j.proenv.2011.07.006 · 2011 · External reference
Unresolved reference
2020 · External reference
Wildfire precursors show complementary predictability in different timescales
10.1038/s41467-023-42597-5 · 2023 · External reference
GHS-SMOD R2023a-GHS settlement layers, application of the degree of urbanisation methodology (stage i) to GHS-pop R2023a and GHS-BUILT-s R2023a, multitemporal (1975–2030)
2023 · External reference
Fire weather indices tailored to regional patterns outperform global models
10.1038/s44304-025-00126-y · 2025 · External reference
Integrating random effects in deep neural networks
2023 · External reference
A systematic analysion tasks
10.1016/j.ipm.2009.03.002 · 2009 · External reference
ERA5 hourly data on single levels from 1940 to present
2024 · External reference
Residual attention network for image classification
2017 · External reference
Identifying key drivers of wildfires in the contiguous US using machine learning and game theory interpretation
10.1029/2020ef001910 · 2021 · External reference
Impacts of disturbance history on forest carbon stocks and fluxes: Merging satellite disturbance mapping with forest inventory data in a carbon cycle model framework
10.1016/j.rse.2013.10.034 · 2014 · External reference
An improved machine-learning model for lightning-ignited wildfire prediction in texas
10.1088/1748-9326/add754 · 2025 · External reference
Unresolved reference
2023 · External reference
Deep neural networks for global wildfire susceptibility modelling
10.1016/j.ecolind.2021.107735 · 2021 · External reference
Dynamic monitoring and analysis of grassland fire based on multi-source satellite remote sensing data
2013 · External reference
Changes in fire activity in africa from 2002 to 2016 and their potential drivers
10.1029/2019gl083469 · 2019 · External reference
The ERA5 global reanalysis
10.1002/qj.3803 · ExternalCitation · doi-reference
Breaking the computational barrier in high-resolution weather forecasting with decoupled training
10.1016/j.aosl.2026.100821 · ExternalCitation · doi-reference
Enhancing prediction of wildfire occurrence and behavior in alaska using spatio-temporal clustering and ensemble machine learning
10.1016/j.ecoinf.2024.102963 · ExternalCitation · doi-reference
A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment
10.1016/j.ecoinf.2025.103325 · ExternalCitation · doi-reference
Predicting wildfire occurrences in Portugal using machine learning classification models
10.1016/j.ecoinf.2025.103455 · ExternalCitation · doi-reference
Deep neural networks for global wildfire susceptibility modelling
10.1016/j.ecolind.2021.107735 · ExternalCitation · doi-reference
Deep learning modeling of human activity affected wildfire risk by incorporating structural features: A case study in eastern China
10.1016/j.ecolind.2024.111946 · ExternalCitation · doi-reference
A systematic analysion tasks
10.1016/j.ipm.2009.03.002 · ExternalCitation · doi-reference
Generation of 30 m resolution monthly burned area product in africa based on landsat 8/9 and sentinel-2 data
10.1016/j.isprsjprs.2025.09.012 · ExternalCitation · doi-reference
Fires dynamics in the pantanal: Impacts of anthropogenic activities and climate change
10.1016/j.jenvman.2021.113586 · ExternalCitation · doi-reference
Spatio-temporal distribution of fire activity in protected areas of sub-saharan africa derived from MODIS data
10.1016/j.proenv.2011.07.006 · ExternalCitation · doi-reference
Impacts of disturbance history on forest carbon stocks and fluxes: Merging satellite disturbance mapping with forest inventory data in a carbon cycle model framework
10.1016/j.rse.2013.10.034 · ExternalCitation · doi-reference
Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions
10.1016/j.scitotenv.2018.03.290 · ExternalCitation · doi-reference
Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model
10.1016/j.scitotenv.2023.163004 · ExternalCitation · doi-reference
Predicting wildfire burns from big geodata using deep learning
10.1016/j.ssci.2021.105276 · ExternalCitation · doi-reference
Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections
10.1016/j.wace.2025.100751 · ExternalCitation · doi-reference
Changes in fire activity in africa from 2002 to 2016 and their potential drivers
10.1029/2019gl083469 · ExternalCitation · doi-reference
Identifying key drivers of wildfires in the contiguous US using machine learning and game theory interpretation
10.1029/2020ef001910 · ExternalCitation · doi-reference
Estimation of fire counts and fire radiative power using satellite optical and microwave vegetation indices with random forest method
10.1029/2024jd041680 · ExternalCitation · doi-reference
21St century drought-related fires counteract the decline of amazon deforestation carbon emissions
10.1038/s41467-017-02771-y · ExternalCitation · doi-reference
Wildfire precursors show complementary predictability in different timescales
10.1038/s41467-023-42597-5 · ExternalCitation · doi-reference
Global data-driven prediction of fire activity
10.1038/s41467-025-58097-7 · ExternalCitation · doi-reference
Human exposure and sensitivity to globally extreme wildfire events
10.1038/s41559-016-0058 · ExternalCitation · doi-reference
LandScan global 30 arcsecond annual global gridded population datasets from 2000 to 2022
10.1038/s41597-025-04817-z · ExternalCitation · doi-reference
FireCastNet: earth-as-a-graph for seasonal fire prediction
10.1038/s41598-025-30645-7 · ExternalCitation · doi-reference
Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast
10.1038/s41612-023-00512-1 · ExternalCitation · doi-reference
Fire weather indices tailored to regional patterns outperform global models
10.1038/s44304-025-00126-y · ExternalCitation · doi-reference
VIIRS night-time lights
10.1080/01431161.2017.1342050 · ExternalCitation · doi-reference
An improved machine-learning model for lightning-ignited wildfire prediction in texas
10.1088/1748-9326/add754 · ExternalCitation · doi-reference
Next day wildfire spread: A machine learning dataset to predict wildfire spreading from remote-sensing data
10.1109/tgrs.2022.3192974 · ExternalCitation · doi-reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · ExternalCitation · doi-reference
The potential predictability of fire danger provided by numerical weather prediction
10.1175/jamc-d-15-0297.1 · ExternalCitation · doi-reference
Finding structure in time
10.1207/s15516709cog1402_1 · ExternalCitation · doi-reference
Advancing wildfire susceptibility mapping through maching learning and shapley additive explanations-integrated geospatial analysis in northern Morocco’s mediterranean region
10.3389/ffgc.2025.1705341 · ExternalCitation · doi-reference
Explainable AI-driven wildfire prediction in Australia: SHAP and feature importance to identify environmental drivers in the age of climate change
10.3390/fire8110421 · ExternalCitation · doi-reference
