Abstract
No public abstract is available from the verified source records.
Xingdong Yan, Congcong Li, Jianwu Xing, Hengze Zhao, Lingxiang Wang
Abstract
Authors
Institutions
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model
10.1016/j.scitotenv.2023.163004 · 2023
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
Enhancing prediction of wildfire occurrence and behavior in alaska using spatio-temporal clustering and ensemble machine learning
10.1016/j.ecoinf.2024.102963 · 2025
21St century drought-related fires counteract the decline of amazon deforestation carbon emissions
10.1038/s41467-017-02771-y · 2018
Predicting wildfire burns from big geodata using deep learning
10.1016/j.ssci.2021.105276 · 2021
Unresolved referenced work
2022
Large-scale machine learning with stochastic gradient descent
2010
Human exposure and sensitivity to globally extreme wildfire events
10.1038/s41559-016-0058 · 2017
Predicting wildfire occurrences in Portugal using machine learning classification models
10.1016/j.ecoinf.2025.103455 · 2025
Provenance
crossref
Confidence 100%
ror
Confidence 99%
openalex
Confidence 95%
datacite
Confidence 0%
Xgboost: A scalable tree boosting system
2016
Unresolved referenced work
2023
Breaking the computational barrier in high-resolution weather forecasting with decoupled training
10.1016/j.aosl.2026.100821 · 2026
Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast
10.1038/s41612-023-00512-1 · 2023
Global data-driven prediction of fire activity
10.1038/s41467-025-58097-7 · 2025
The potential predictability of fire danger provided by numerical weather prediction
10.1175/jamc-d-15-0297.1 · 2016
Estimation of fire counts and fire radiative power using satellite optical and microwave vegetation indices with random forest method
10.1029/2024jd041680 · 2025
A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment
10.1016/j.ecoinf.2025.103325 · 2025
Finding structure in time
10.1207/s15516709cog1402_1 · 1990
VIIRS night-time lights
10.1080/01431161.2017.1342050 · 2017
Forest fire probability zonation using dNBR and machine learning models: a case study at the similipal biosphere reserve (SBR), odisha, India
2025
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
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
The ERA5 global reanalysis
10.1002/qj.3803 · 2020
Long short-term memory
10.1162/neco.1997.9.8.1735 · 1997
Unresolved referenced work
2013
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
Next day wildfire spread: A machine learning dataset to predict wildfire spreading from remote-sensing data
10.1109/tgrs.2022.3192974 · 2022
LandScan global 30 arcsecond annual global gridded population datasets from 2000 to 2022
10.1038/s41597-025-04817-z · 2025
A unified approach to interpreting model predictions
2017
Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions
10.1016/j.scitotenv.2018.03.290 · 2018
Fires dynamics in the pantanal: Impacts of anthropogenic activities and climate change
10.1016/j.jenvman.2021.113586 · 2021
FLDAS noah land surface model L4 global monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS)(fldas_noah01_c_gl_m)
2018
FireCastNet: earth-as-a-graph for seasonal fire prediction
10.1038/s41598-025-30645-7 · 2025
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
Unresolved referenced work
Kept as external metadata until matched
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
Unresolved referenced work
2020
Wildfire precursors show complementary predictability in different timescales
10.1038/s41467-023-42597-5 · 2023
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
Fire weather indices tailored to regional patterns outperform global models
10.1038/s44304-025-00126-y · 2025
Changes in fire activity in africa from 2002 to 2016 and their potential drivers
10.1029/2019gl083469 · doi-reference
Deep neural networks for global wildfire susceptibility modelling
10.1016/j.ecolind.2021.107735 · doi-reference
An improved machine-learning model for lightning-ignited wildfire prediction in texas
10.1088/1748-9326/add754 · 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 · doi-reference
Identifying key drivers of wildfires in the contiguous US using machine learning and game theory interpretation
10.1029/2020ef001910 · doi-reference
A systematic analysion tasks
10.1016/j.ipm.2009.03.002 · doi-reference
Fire weather indices tailored to regional patterns outperform global models
10.1038/s44304-025-00126-y · doi-reference
Wildfire precursors show complementary predictability in different timescales
10.1038/s41467-023-42597-5 · 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 · 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 · doi-reference
FireCastNet: earth-as-a-graph for seasonal fire prediction
10.1038/s41598-025-30645-7 · doi-reference
Fires dynamics in the pantanal: Impacts of anthropogenic activities and climate change
10.1016/j.jenvman.2021.113586 · 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 · doi-reference
LandScan global 30 arcsecond annual global gridded population datasets from 2000 to 2022
10.1038/s41597-025-04817-z · doi-reference
Next day wildfire spread: A machine learning dataset to predict wildfire spreading from remote-sensing data
10.1109/tgrs.2022.3192974 · 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 · doi-reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · doi-reference
The ERA5 global reanalysis
10.1002/qj.3803 · 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 · 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 · doi-reference
VIIRS night-time lights
10.1080/01431161.2017.1342050 · doi-reference
Finding structure in time
10.1207/s15516709cog1402_1 · doi-reference
A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment
10.1016/j.ecoinf.2025.103325 · doi-reference
Estimation of fire counts and fire radiative power using satellite optical and microwave vegetation indices with random forest method
10.1029/2024jd041680 · doi-reference
The potential predictability of fire danger provided by numerical weather prediction
10.1175/jamc-d-15-0297.1 · doi-reference
Global data-driven prediction of fire activity
10.1038/s41467-025-58097-7 · doi-reference
Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast
10.1038/s41612-023-00512-1 · doi-reference
Breaking the computational barrier in high-resolution weather forecasting with decoupled training
10.1016/j.aosl.2026.100821 · doi-reference
Predicting wildfire occurrences in Portugal using machine learning classification models
10.1016/j.ecoinf.2025.103455 · doi-reference
Human exposure and sensitivity to globally extreme wildfire events
10.1038/s41559-016-0058 · doi-reference
Predicting wildfire burns from big geodata using deep learning
10.1016/j.ssci.2021.105276 · doi-reference
21St century drought-related fires counteract the decline of amazon deforestation carbon emissions
10.1038/s41467-017-02771-y · 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 · 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 · doi-reference
Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model
10.1016/j.scitotenv.2023.163004 · doi-reference