用雷达影像和环境数据，预测肯尼亚河流流域的洪水易发区。
Predictive Modeling of Flood-Prone Areas Using SAR and Environmental Variables
- 融合哨兵1号雷达与地形、土地利用等变量，训练机器学习模型。
- 随机森林模型准确率达76.2%，优于其他三种方法。
- 识别出维多利亚湖附近低地为高风险区，适合防灾规划参考。
洪水是全球最具破坏性的自然灾害之一，严重威胁生态系统、基础设施和人类生计。本研究结合合成孔径雷达（SAR）影像与环境及水文数据，对肯尼亚西部尼扬多河流域的洪水易发性进行建模。利用2024年5月洪灾期间的哨兵-1双极化SAR数据生成二值洪水分布图，作为机器学习模型的训练数据。整合坡度、高程、朝向、土地利用/覆被、土壤类型和距河流距离共六项影响因子，训练了四种监督分类器：逻辑回归（LR）、分类与回归树（CART）、支持向量机（SVM）和随机森林（RF）。模型性能通过准确率、Cohen's Kappa系数及受试者工作特征（ROC）分析评估。结果表明，随机森林表现最佳（准确率=0.762；Kappa=0.480），优于LR、CART和SVM。基于RF的易发性地图显示，靠近维多利亚湖的低洼卡诺平原风险最高，与历史洪灾记录及2024年事件影响一致。研究证实，结合SAR数据与集成学习方法在数据匮乏地区具有重要应用价值，所生成的地图可为减灾、土地利用规划和预警系统提供关键支持。
原文摘要 · Abstract (English)
Flooding is one of the most destructive natural hazards worldwide, posing serious risks to ecosystems, infrastructure, and human livelihoods. This study combines Synthetic Aperture Radar (SAR) imagery with environmental and hydrological data to model flood susceptibility in the River Nyando watershed, western Kenya. Sentinel-1 dual-polarization SAR data from the May 2024 flood event were processed to produce a binary flood inventory, which served as training data for machine learning (ML) models. Six conditioning factors -- slope, elevation, aspect, land use/land cover, soil type, and distance from streams -- were integrated with the SAR-derived flood inventory to train four supervised classifiers: Logistic Regression (LR), Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF). Model performance was assessed using accuracy, Cohen's Kappa, and Receiver Operating Characteristic (ROC) analysis. Results indicate that RF achieved the highest predictive performance (accuracy = 0.762; Kappa = 0.480), outperforming LR, CART, and SVM. The RF-based susceptibility map showed that low-lying Kano Plains near Lake Victoria have the highest flood vulnerability, consistent with historical flood records and the impacts of the May 2024 event. These findings demonstrate the value of combining SAR data and ensemble ML methods for flood susceptibility mapping in regions with limited data. The resulting maps offer important insights for disaster risk reduction, land-use planning, and early warning system development.
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