This article proposes and verifies a novel intelligence approach for modelling forest fire danger, namely ICA-RVM, developed based on Relevance Vector Machine (RVM) and Imperialist Competitive Algorithm (ICA), state-of-the art machine learning techniques that have not been investigated for forest fire danger modeling. RVM is used to establish a prediction model that computes probability of fire danger, whereas ICA is adopted to optimize the prediction model. The tropical forest at Gia Lai province, Central Highland (Vietnam), was used as a case study. Area under the curve (AUC) and statistical measures were used to assess the model performance. The result showed that the proposed model achieves high performances; AUC is 0.842 and 0.793 on the training dataset and the validation dataset, respectively. Compared to two benchmarks, Random Forests and Support Vector Machine, the proposed model performs better. Therefore, the propose ICA-RVM is a valid alternative system for forest fire danger modeling.

中文翻译：

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基于相关向量机和帝国主义竞争优化的混合智能系统，用于使用 GIS 模拟森林火灾危险

本文提出并验证了一种新的森林火灾危险建模智能方法，即 ICA-RVM，它基于相关向量机 (RVM) 和帝国主义竞争算法 (ICA) 开发，这是尚未被发现的最先进的机器学习技术。进行森林火灾危险建模研究。RVM用于建立计算火灾危险概率的预测模型，而ICA用于优化预测模型。中部高地（越南）嘉莱省的热带森林被用作案例研究。曲线下面积 (AUC) 和统计量度用于评估模型性能。结果表明，所提出的模型实现了高性能；训练数据集和验证数据集的 AUC 分别为 0.842 和 0.793。与两个基准相比，随机森林和支持向量机，所提出的模型表现更好。因此，所提出的 ICA-RVM 是一种有效的森林火灾危险建模替代系统。