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.

预测野火传播在减少此类事件的影响方面起着至关重要的作用。各种机器学习 (ML) 方法，即支持向量回归 (SVR)、高斯过程回归 (GPR)、回归树和神经网络 (NN)，用于了解它们在开发模型以预测草火蔓延速度的适用性. 来自野火和实验火灾的数据集包含 283 条记录，具有 7 个特征，被编译并用于开发和评估基于 ML 的模型。这些模型非常适合模型开发数据集。模型对评估数据集的拟合导致更高的错误，其中一些模型对模型开发数据集产生的错误最低，对评估数据集产生最高的错误。评估了基于 ML 的最佳模型与运营模型的预测性能。SHAP 可视化工具用于确定表现最佳的模型中最有影响力的变量。