不确定近似骨架蚁群聚类算法在滑坡危险性预测中的研究与应用

摘要

受不确定因素降雨难以准确处理的制约以及蚁群聚类算法在搜索空间容易陷入局部最优解和搜索速度慢的特征影响, 为了提高滑坡危险性预测的精度, 提出一种不确定近似骨架蚁群聚类算法.首先采用Gauss点概率模型来描述不确定数据, 对不确定数据进行相似性度量;其次引入信息素重分配和自适应动态变量实现蚁群聚类算法局部信息素和全局信息素更新, 提高蚁群聚类算法搜索速度, 加载遗传算法避免蚁群聚类算法过早陷入局部最优;最后结合近似骨架理论, 构建不确定近似骨架蚁群聚类算法模型, 缩减迭代次数, 快速搜索出聚类结果.在UCI真实数据集和延安宝塔区滑坡实验数据集上的实验结果显示, 不确定近似骨架蚁群聚类算法具有较高的聚类质量, 预测精度达到93.3％, 验证了算法在滑坡危险性预测中的可行性.%The uncertain factor rainfall is hard to accurately handle and the ant colony clustering algorithm is easy to get caught into sub-optimal solution and the searching speed is low in searching space.In order to improve the prediction accuracy of landslide hazard, we propose an uncertain ant colony clustering algorithm based on approximate backbone.Firstly, it utilizes the Gauss point probability model to describe the uncertain data and measure their similarity.Secondly, we introduce the pheromone redistribution and adaptive dynamic variables to update the local pheromone and global pheromone for improving the algorithm's searching speed, and load the genetic algorithm to prevent it from falling into local optimum early.Finally, combining the approximate backbone theory, we build an uncertain ant colony clustering algorithm model based on approximate backbone, which reduces the iteration times and obtains the clustering solution rapidly.Experiments on UCI true datasets and landslide experiment datasets of the Baota district of Yan'an show that the proposed method achieves a higher clustering quality and the prediction accuracy reaches 93.3％, which verifies its feasibility.