Comparative analysis of linear regression and machine learning models for dead fuel moisture content prediction in Golestan Province forests in northeast Iran

Abstract

Accurate prediction of dead fuel moisture content (FMC) is critical for wildfire management, particularly in the Hyrcanian forests of Iran. This study evaluates the performance of machine learning models - Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GBoost), and Convolutional Neural Network (CNN) versus traditional linear regression methods in predicting FMC for three fuel classes (1-hr, 10-hr, and litter) over Golestan province, NE Iran. Using data collected from 235 plots between March and November 2023, we incorporated meteorological variables including temperature (T), relative humidity (RH), and wind speed (WS), along with topographic features, into the models. The results showed that multivariable models considerably outperformed the univariate models, and machine learning models were more accurate than the linear regression models. The most accurate model was RF reaching an adjusted coefficient of determination (R2adj) of 97.08 and a relative RMSE of 5.93%, considering the training data. Meanwhile, with the test data, RF obtained an R2adj of 87.99, with a relative RMSE of 10.44%. Furthermore, the performance of the SVMs is very good, with an R2adj of 85.40 for the training data and 86.86 for the test data. In contrast, the linear regression models had lower accuracy, with the best performance, from univariate models being RH with a R2adj of 66.70 and a relative RMSE of 18.90%. Multivariable regression models combining RH, T and vapor pressure deficit (VPD) improved their performance, but still fell short of machine learning models. The results show that RH and VPD were the most important variables for FMC prediction, especially for fine fuels. The machine learning models showed excellent performance due to their capabilities for describing nonlinear relationships and performing well with high-dimensional data enhancing FMC predictions by up to 31% over traditional methods. This study advances the understanding of FMC dynamics by demonstrating the enhanced accuracy of machine learning models in FMC prediction, here studied for complex temperate forest ecosystems. By highlighting the importance of RH and VPD as critical predictors, the findings contribute to the growing body of knowledge on wildfire risk assessment. Still these results underscore the need for further research to refine models and explore their applicability in diverse environments and under varying climatic conditions.

Citation

Alhaj-Khalaf, Mhd. Wathek; Jouibary, Shaban Shataee; Jahdi, Roghayeh; Jolly, William M. 2025. Comparative analysis of linear regression and machine learning models for dead fuel moisture content prediction in Golestan Province forests in northeast Iran. Austrian Journal of Forest Science. https://doi.org/10.53203/fs.2501.1.