{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T12:33:51Z","timestamp":1763123631102,"version":"3.45.0"},"reference-count":32,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Wildfires pose a growing threat to ecosystems, infrastructure, and public safety, particularly in the province of British Columbia (BC), Canada. In recent years, the frequency, severity, and scale of wildfires in BC have increased significantly, largely due to climate change, human activity, and changing land use patterns. This study presents a comprehensive, data-driven approach to wildfire prediction, leveraging advanced machine learning (ML) and deep learning (DL) techniques. A high-resolution dataset was constructed by integrating five years of wildfire incident records from the Canadian Wildland Fire Information System (CWFIS) with ERA5 reanalysis climate data. The final dataset comprises more than 3.6 million spatiotemporal records and 148 environmental, meteorological, and geospatial features. Six feature selection techniques were evaluated, and five predictive models\u2014Random Forest, XGBoost, LightGBM, CatBoost, and an RNN + LSTM\u2014were trained and compared. The CatBoost model achieved the highest predictive performance with an accuracy of 93.4%, F1-score of 92.1%, and ROC-AUC of 0.94, while Random Forest achieved an accuracy of 92.6%. The study identifies key environmental variables, including surface temperature, humidity, wind speed, and soil moisture, as the most influential predictors of wildfire occurrence. These findings highlight the potential of data-driven AI frameworks to support early warning systems and enhance operational wildfire management in British Columbia.<\/jats:p>","DOI":"10.3390\/bdcc9110290","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T11:49:43Z","timestamp":1763120983000},"page":"290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wildfire Prediction in British Columbia Using Machine Learning and Deep Learning Models: A Data-Driven Framework"],"prefix":"10.3390","volume":"9","author":[{"given":"Maryam","family":"Nasourinia","sequence":"first","affiliation":[{"name":"School of Engineering and Computer Science, Laurentian University, Sudbury, ON P3E 2C6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7155-7901","authenticated-orcid":false,"given":"Kalpdrum","family":"Passi","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Laurentian University, Sudbury, ON P3E 2C6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/j.1365-2486.2008.01660.x","article-title":"Impacts of climate change on fire activity and fire management in the circumboreal forest","volume":"15","author":"Flannigan","year":"2009","journal-title":"Glob. Change Biol."},{"key":"ref_2","first-page":"985","article-title":"Forest fire occurrence and climate change in Canada","volume":"26","author":"Wotton","year":"2017","journal-title":"Int. J. Wildland Fire"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1139\/cjfr-2018-0293","article-title":"Fire-regime changes in Canada over the last half century","volume":"49","author":"Hanes","year":"2019","journal-title":"Can. J. For. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"L18211","DOI":"10.1029\/2004GL020876","article-title":"Detecting the effect of climate change on Canadian forest fires","volume":"31","author":"Gillett","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","unstructured":"Hirsch, K., Fuglem, P., and Kafka, V. (2021). Wildfire Management in Canada: Review and Perspectives, Canadian Forest Service."},{"key":"ref_6","unstructured":"Government of Canada (2025, April 11). Wildfire Causes and Risks, Available online: https:\/\/www.nrcan.gc.ca\/climate-change\/impacts-adaptations\/climate-change-impacts\/wildfires\/10771."},{"key":"ref_7","unstructured":"NASA FIRMS (2025, September 01). Fire Information for Resource Management System, Available online: https:\/\/firms.modaps.eosdis.nasa.gov\/."},{"key":"ref_8","unstructured":"Government of British Columbia (2025, April 11). Wildfire Management Branch, Available online: https:\/\/www2.gov.bc.ca\/gov\/content\/safety\/wildfire-status."},{"key":"ref_9","unstructured":"Parisien, M.A., Kafka, V., Hirsch, K.G., Todd, J.B., Lavoie, S.G., and Maczek, P.D. (2005). Mapping Wildfire Susceptibility with the Burn-P3 Simulation Model, Canadian Forest Service. Information Report NOR-X-405."},{"key":"ref_10","unstructured":"BC Wildfire Service (2025, May 11). Wildfire Statistics and Information, Available online: https:\/\/www2.gov.bc.ca\/."},{"key":"ref_11","unstructured":"FireSmart Canada (2025, May 11). FireSmart Begins at Home Manual. Available online: https:\/\/www.firesmartcanada.ca."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1139\/x89-237","article-title":"A logistic model for predicting daily people-caused forest fire occurrence in Ontario","volume":"19","author":"Martell","year":"1989","journal-title":"Can. J. For. Res."},{"key":"ref_13","unstructured":"Finney, M.A. (2004). FARSITE: Fire Area Simulator\u2013Model Development and Evaluation, RMRS-RP-4 Revised."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1890\/07-1183.1","article-title":"Climate and wildfire area burned in western U.S. ecoprovinces, 1916\u20132003","volume":"19","author":"Littell","year":"2009","journal-title":"Ecol. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Szpakowski, D.M., and Jensen, J.L.R. (2019). A Review of the Applications of Remote Sensing in Fire Ecology. Remote Sens., 11.","DOI":"10.3390\/rs11222638"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1002\/jgrg.20042","article-title":"Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4)","volume":"118","author":"Giglio","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A review of machine learning applications in wildfire science and management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","first-page":"636","article-title":"Wildfire susceptibility mapping using deep learning in Canada","volume":"10","author":"Zhang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Omar, N., Al-zebari, A., and Sengur, A. (2021, January 16\u201317). Deep learning approach to predict forest fires using meteorological measurements. Proceedings of the 2021 2nd International Informatics and Software Engineering Conference (IISEC), Ankara, Turkey.","DOI":"10.1109\/IISEC54230.2021.9672446"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.scitotenv.2018.02.278","article-title":"Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models: The case of Dayu County, China","volume":"630","author":"Hong","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"264","DOI":"10.5220\/0012363000003636","article-title":"Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling","volume":"Volume 2","author":"Tavakoli","year":"2024","journal-title":"Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Elsarrar, O., Darrah, M., and Devine, R. (2019, January 16\u201319). Analysis of forest fire data using neural network rule extraction with human understandable rules. Proceedings of the 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2019.00308"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"241","DOI":"10.55525\/tjst.1063284","article-title":"Comparison of the machine learning methods to predict wildfire areas","volume":"17","author":"Bayat","year":"2022","journal-title":"Turk. J. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sharma, U., Shaw, S., Kumari, K.S., Shailendra, A., Bengani, C., and Ramesh, S. (2023, January 30\u201331). Forest fire prediction using supervised machine learning algorithms. Proceedings of the International Conference on Recent Trends in Data Science and Its Applications (ICRTDA 2023), Kattankulathur, India.","DOI":"10.13052\/rp-9788770040723.114"},{"key":"ref_26","first-page":"5612650","article-title":"Application of the artificial neural network and support vector machines in forest fire prediction in the Guangxi Autonomous Region, China","volume":"2020","author":"Li","year":"2020","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_27","first-page":"100627","article-title":"Artificial intelligence in environmental monitoring: A comprehensive review","volume":"18","author":"Olawade","year":"2024","journal-title":"Environ. Adv."},{"key":"ref_28","first-page":"56573","article-title":"Fern\u00e1ndez-Torres, M.; Cohrs, K.-H.; H\u00f6hl, A.; Castelletti, A.; Pacal, A.; Robin, C.; Martinuzzi, F.; Papoutsis, I.; Prapas, I.; et al. Artificial intelligence for modeling and understanding extreme climate events","volume":"16","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Forouheshfar, Y., Ayadi, R., and Moghadas, O. (2025). Enhancing system resilience to climate change through artificial intelligence: A systematic literature review. Front. Clim., 7.","DOI":"10.3389\/fclim.2025.1585331"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, X., Chen, H., and Zhang, Y. (2025). Machine learning-based prediction of resilience in green supply chain systems. Systems, 13.","DOI":"10.3390\/systems13070615"},{"key":"ref_31","unstructured":"Canadian Wildland Fire Information System (CWFIS) (2025, April 11). Natural Resources Canada. Available online: https:\/\/cwfis.cfs.nrcan.gc.ca\/."},{"key":"ref_32","unstructured":"Environment Canada (2025, April 11). Historical Climate Data. Available online: https:\/\/climate.weather.gc.ca\/."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/11\/290\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T12:30:54Z","timestamp":1763123454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/11\/290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":32,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["bdcc9110290"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9110290","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]}}}