机器学习在森林火灾预测方面的应用

Application of Machine Learning in Forest Fire Prediction

森林火灾的发生对社会经济和自然环境造成了威胁,因此及时预测森林火灾的发生具有重要意义。目前,机器学习被广泛用于各个行业,基于此本文应用神经网络、随机森林、逻辑回归3种机器学习算法,建立森林火灾预测模型,并用此模型对阿尔及利亚Bejaia地区和Sidi Bel-abbesl地区采集到的数据进行预测。实验结果证明,3种机器学习算法对森林火灾风险预测均有较好表现,综合准确率可达95.9%,其中随机森林算法在准确度方面表现最好,逻辑回归算法在运算效率方面表现最好,利用该森林火灾预测模型在防范森林火灾方面具有一定的可行性。

The occurrence of forest fire poses a threat to social economy and natural environment. Therefore, it is of great significance to predict the occurrence of forest fire in time. At present, machine learning is widely used in various industries. Based on this, this paper establishes a forest fire prediction model by using three machine learning algorithms: neural network, random forest and logistic regression, and uses this model to predict the data collected in Bejaia and Sidi Bel abbesl areas of Algeria. The experimental results show that the three machine learning algorithms have good performance in forest fire risk prediction, and the comprehensive accuracy can reach 95.9%. Among them, the random forest algorithm performs best in accuracy and the logical regression algorithm performs best in operation efficiency. Using the forest fire prediction model has certain feasibility in preventing forest fire.