福州大学紫金地质与矿业学院
纸质出版:2026
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豆红强, 刘银, 王浩, 等. 基于异质集成机器学习模型的海岸带滑坡灾害易发性评价与区划[J]. 地质科技通报, 2026,45(2):338-350.
豆红强, 刘银, 王浩, et al. 基于异质集成机器学习模型的海岸带滑坡灾害易发性评价与区划[J]. Bulletin of Geological Science and Technology, 2026, 45(2): 338-350.
豆红强, 刘银, 王浩, 等. 基于异质集成机器学习模型的海岸带滑坡灾害易发性评价与区划[J]. 地质科技通报, 2026,45(2):338-350. DOI: 10.19509/j.cnki.dzkq.tb20240567.
豆红强, 刘银, 王浩, et al. 基于异质集成机器学习模型的海岸带滑坡灾害易发性评价与区划[J]. Bulletin of Geological Science and Technology, 2026, 45(2): 338-350. DOI: 10.19509/j.cnki.dzkq.tb20240567.
随着海洋工程建设的快速推进和极端天气事件频发,海岸带滑坡的风险显著增加。然而,现有关于滑坡易发性区划的研究多集中于内陆山地滑坡,对海岸带滑坡灾害的易发性评价尚缺乏系统研究。以福建省海岸带为研究区,通过收集海岸带滑坡历史数据,利用信息增益比法和皮尔森相关系数法构建适用于海岸带滑坡的易发性评价指标体系。以粒子群优化支持向量机(PSO-SVM)和随机森林(RF)为基学习器,构建Stacking异质集成学习模型,开展福建省海岸带滑坡的易发性评价和区划研究,探讨不同训练集与测试集划分比例对异质集成模型预测精度的影响。结果表明:Stacking异质集成学习模型在训练-测试集比例为70∶30时表现最佳,其准确度、精确度、召回率、F1分数值分别为0.869
0.842
0.909
0.874,其中准确度、精确度与F1分数较其他模型提升了最高0.198
0.227和0.140,其受试者工作特征(ROC)曲线下方面积(area under the curve,简称AUC)值为0.938,较其他模型提高了0.019~0.216;表明Stacking异质集成模型在海岸带滑坡易发性评价中具有较强的适用性和优越性。
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