洪水作为全球范围内频发的自然灾害,其发生频率和严重程度受多种因素影响。本研究基于包含超过百万洪水事件数据的大规模数据集,涉及20个关键指标,通过斯皮尔曼相关性分析及随机森林分类模型,精确识别出对洪水概率影响显著的因素。为克...洪水作为全球范围内频发的自然灾害,其发生频率和严重程度受多种因素影响。本研究基于包含超过百万洪水事件数据的大规模数据集,涉及20个关键指标,通过斯皮尔曼相关性分析及随机森林分类模型,精确识别出对洪水概率影响显著的因素。为克服多重共线性及过拟合问题,本文创新性的提出一种结合主成分分析(PCA)与梯度提升算法(XGBoost)的复合预测模型——PCA-XGBoost洪水预测模型。此模型通过降维处理显著减少了输入变量之间的相互依赖性,同时优化了XGBoost的超参数以增强其预测性能和泛化能力。通过严格的交叉验证和参数调优,PCA-XGBoost模型在洪水预测精度和操作效率上展现了卓越性能,为全球多地区洪水预警系统提供了科学依据和有效工具。此研究不仅推动了洪水风险评估模型的创新,也为相关政策制定和灾害管理提供了理论支持。Floods, as a globally frequent natural disaster, are influenced by various factors in terms of frequency and severity. This study, based on a large dataset containing over one million flood events and involving 20 key indicators, accurately identifies significant factors affecting flood probability through Spearman correlation analysis and random forest classification models. To overcome issues of multicollinearity and overfitting, this paper innovatively proposes a composite prediction model combining Principal Component Analysis (PCA) and the Gradient Boosting algorithm (XGBoost)—the PCA-XGBoost Flood Prediction Model. This model significantly reduces interdependencies among input variables through dimensionality reduction and optimizes the hyperparameters of XGBoost to enhance its predictive performance and generalization ability. With rigorous cross-validation and parameter tuning, the PCA-XGBoost model demonstrates superior performance in flood prediction accuracy and operational efficiency, providing a scientific basis and effective tools for flood warning systems in various global regions. This research not only advances the innovation of flood risk assessment models but also offers theoretical support for policy-making and disaster management.展开更多
文摘洪水作为全球范围内频发的自然灾害,其发生频率和严重程度受多种因素影响。本研究基于包含超过百万洪水事件数据的大规模数据集,涉及20个关键指标,通过斯皮尔曼相关性分析及随机森林分类模型,精确识别出对洪水概率影响显著的因素。为克服多重共线性及过拟合问题,本文创新性的提出一种结合主成分分析(PCA)与梯度提升算法(XGBoost)的复合预测模型——PCA-XGBoost洪水预测模型。此模型通过降维处理显著减少了输入变量之间的相互依赖性,同时优化了XGBoost的超参数以增强其预测性能和泛化能力。通过严格的交叉验证和参数调优,PCA-XGBoost模型在洪水预测精度和操作效率上展现了卓越性能,为全球多地区洪水预警系统提供了科学依据和有效工具。此研究不仅推动了洪水风险评估模型的创新,也为相关政策制定和灾害管理提供了理论支持。Floods, as a globally frequent natural disaster, are influenced by various factors in terms of frequency and severity. This study, based on a large dataset containing over one million flood events and involving 20 key indicators, accurately identifies significant factors affecting flood probability through Spearman correlation analysis and random forest classification models. To overcome issues of multicollinearity and overfitting, this paper innovatively proposes a composite prediction model combining Principal Component Analysis (PCA) and the Gradient Boosting algorithm (XGBoost)—the PCA-XGBoost Flood Prediction Model. This model significantly reduces interdependencies among input variables through dimensionality reduction and optimizes the hyperparameters of XGBoost to enhance its predictive performance and generalization ability. With rigorous cross-validation and parameter tuning, the PCA-XGBoost model demonstrates superior performance in flood prediction accuracy and operational efficiency, providing a scientific basis and effective tools for flood warning systems in various global regions. This research not only advances the innovation of flood risk assessment models but also offers theoretical support for policy-making and disaster management.
