%0 Journal Article
%T 基于K-Means聚类等综合模型对洪水灾害的数据分析与预测
Data Analysis and Prediction of Flood Disasters Based on Comprehensive Models Such as K-Means Clustering
%A 邵建鑫
%J Advances in Applied Mathematics
%P 580-588
%@ 2324-8009
%D 2025
%I Hans Publishing
%R 10.12677/aam.2025.145284
%X 在当今社会，全球气候变化对人类生活和环境造成了日益严重的影响，特别是频发的极端天气事件如洪水给社会经济发展带来了巨大挑战。洪水不仅造成了财产损失和人员伤亡，还对基础设施、农业生产和生态环境造成长期影响。因此，准确评估洪水风险并采取有效的预防和应对措施至关重要。本文基于K-means聚类和支持向量机(SVM)等方法，分析了洪水发生的多因素影响，并建立了洪水风险评估模型。首先，我们通过灰色关联分析和数据挖掘技术，识别了影响洪水发生的关键因素，并对这些因素进行了量化和分析。其次，我们应用K-means聚类方法对不同风险级别的洪水进行了分类和评估。最后，通过支持向量机(SVM)模型，我们预测了洪水发生概率，并评估了模型的预测准确性和稳定性。
In today’s society, global climate change has exerted increasingly severe impacts on human life and the environment, particularly with the frequent occurrence of extreme weather events such as floods, which pose significant challenges to socio-economic development. Floods not only cause property damage and casualties, but also have long-term effects on infrastructure, agricultural production, and the ecological environment. Therefore, accurately assessing flood risks and implementing effective prevention and response measures are of paramount importance. This paper analyzes the multi-factor influences on flood occurrence and establishes a flood risk assessment model based on methods such as K-means clustering and Support Vector Machine (SVM). First, we identify key factors affecting flood occurrence through grey relational analysis and data mining techniques, quantifying and analyzing these factors. Second, we apply the K-means clustering method to classify and evaluate floods of different risk levels. Finally, using the Support Vector Machine (SVM) model, we predict the probability of flood occurrence and assess the model’s predictive accuracy and stability.
%K K-Means聚类，
%K 洪水灾害，
%K 灰色关联分析
K-Means Clustering
%K Flood Disaster
%K Grey Correlation Analysis
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=116122