Prediction of wildfire rate of spread in grasslands using machine learning methods

Sadegh Khanmohammadi, Mehrdad Arashpour, Emadaldin Mohammadi Golafshani, Miguel G. Cruz, Abbas Rajabifard, Yu Bai

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)


Prediction of wildfire propagation plays a crucial role in reducing the impacts of such events. Various machine learning (ML) approaches, namely Support Vector Regression (SVR), Gaussian Process Regression (GPR), Regression Tree, and Neural Networks (NN), were used to understand their applicability in developing models to predict the rate of spread of grassfires. A dataset from both wildfires and experimental fires comprising 283 records with 7 features was compiled and utilized to develop and evaluate ML-based models. These models produced excellent fits to the model development dataset. Model fit against the evaluation dataset resulted in higher errors, with some of the models that yielded the lowest error against the model development dataset, producing the highest errors against the evaluation dataset. The predictive performance of the best ML-based models against that of operational models was evaluated. The SHAP visualization tool was used to determine the most influential variables in the best-performing models.

Original languageEnglish
Article number105507
Number of pages12
JournalEnvironmental Modelling and Software
Publication statusPublished - Oct 2022


  • Artificial intelligence
  • Bushfire and wildfire
  • Machine learning
  • Rate of fire spread
  • Remote regions
  • SHAP sensitivity analysis

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