林火的准确预测对其预防与管理具有重要意义。基于福建省2010—2020年林火和气象数据,分别采用Logistic回归模型和基于6种平滑样条基[高斯过程平滑样条基(GP)、三次回归样条基(CR)、薄板回归样条基(TP)、Duchon样条基(DS)、B-样条基(BS)、P-样条基(PS)]拟合的广义加性模型进行林火预测,并对各模型的预测效果进行评价。结果显示:1)Logistic回归模型在训练集上的准确率为74.80%,在测试集上的准确率为75.97%。广义加性模型的预测精度整体优于Logistic回归模型,其中由TP样条基拟合的广义加性模型表现最佳,其训练集和测试集的准确率分别比Logistic回归模型分别提高了3.86%和2.52%。2)基于最优广义加性模型预测结果,对福建省的森林火险等级进行划分。结果表明,中高火险区主要集中在福建省西北和东南地区,西部和东部地区为低火险区。广义加性模型能够更好地捕捉复杂的非线性关系,适用于复杂环境下的林火预测。

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.