Abstract:

Predicting forest fire occurrences is crucial for fire prevention and management.This study used historical forest fire and meteorological data from Fujian Province(from 2010 to 2020)to apply the Logistic Regression Model and Generalized Additive Model(GAM)with six types of smooth spline bases[Gaussian Process Smoothing Spline Basis(GP),Cubic Regression Spline Basis(CR),Thin Plate Regression Spline Basis(TP),Duchon Spline Basis(DS),B-Spline Basis(BS),and P-Spline Basis(PS)]to predict forest fire occurrences.By comparing the performance of these models,their effectiveness in forest fire prediction was evaluated.The results indicate the following.1)The logistic regression model achieved an accuracy of 74.80% on the training set and 75.97% on the test set,demonstrating its baseline performance.Overall,the predictive accuracy of the GAM was generally superior to that of the Logistic Regression Model,with the TP spline basis-based GAM performing the best.Its accuracy on the training and test sets was improved by 3.86% and 2.52%,respectively,compared to the logistic regression model.2)Based on the optimal GAM,the forest fire risk levels in Fujian Province are delineated.The results revealed that areas with moderate to high fire risk are primarily concentrated in the northwest and southeast regions,while the Western and Eastern regions exhibit low fire risk.GAM arebetter at capturing complex nonlinear relationships,making them suitable for predicting forest fire occurrences in complex ecological environments.

Key words: forest fire prediction, logistic regression model, generalized additive model, thin plate regression spline