热带地理2026，Vol.46Issue(3)：483-494,12.DOI:10.13284/j.cnki.rddl.20250382
融合CTGAN与机器学习的温州市台风灾害损失评估方法研究
Typhoon Disaster Loss Assessment Method for Wenzhou City by Integrating CTGAN and Machine Learning
摘要
Abstract
Accurate assessment of typhoon-induced disaster losses is often hindered by limited historical data and severe class imbalances,especially in regions with infrequent but high-impact events.These challenges reduce the robustness and generalizability of predictive models,leading to unreliable assessments of potential disaster severity.To address these issues,this study proposes an integrated evaluation method that combines data augmentation using a CTGAN with multiple machine learning algorithms.The objective was to enhance sample diversity,alleviate class imbalance,and improve the accuracy and stability of disaster loss predictions.Wenzhou City,located in Zhejiang Province,China,was selected as the study area because of its frequent exposure to typhoon-related hazards.Twenty typhoon cases from 1994 to 2020 were collected,and a structured dataset was constructed using 13 key indicators.These indicators cover three dimensions:(1)hazard-inducing factors such as maximum wind speed and accumulated rainfall;(2)environmental background conditions,including elevation,river network density,and landform;and(3)socioeconomic exposure and vulnerability,reflected by variables such as population density,GDP per capita,and infrastructure indicators,such as road length and hospital bed count.To represent the level of disaster impact for each event quantitatively,a disaster loss index was calculated and used as the input for k-means clustering.This unsupervised learning approach classified 20 typhoon events into four distinct loss severity levels,forming the basis for subsequent supervised classification tasks.To overcome the limitations of class imbalance,the CTGAN model was employed to generate synthetic samples under specific class-conditional constraints.The generated samples were incorporated into the training set to enrich underrepresented classes and improve the representativeness of the dataset.Five widely used machine-learning models were trained and evaluated:GBDT,XGBoost,LightGBM,CatBoost,and Random Forest.The experimental results demonstrated that the GBDT model outperformed the others in terms of both classification accuracy and generalization performance.This model showed the most consistent results across multiple metrics,including mAP,precision,recall,F1-score,and accuracy.Additionally,a comparative analysis was conducted to explore the influence of synthetic data volume on model performance.The findings revealed that simply increasing the number of synthetic samples does not guarantee continuous improvement;rather,an optimal range of sample sizes exists beyond which model stability may plateau or even decline.This study provides a practical and scalable methodological framework for typhoon disaster loss assessments in data-constrained environments.By leveraging generative modeling and ensemble learning techniques,this study offers insights into the effective application of data-driven methods to support disaster preparedness,emergency response planning,and resilience analysis in other hazard-prone regions.关键词
台风/灾害评估/机器学习/数据增强/温州市Key words
typhoon/disaster assessment/machine learning/data augmentation/Wenzhou City分类
天文与地球科学引用本文复制引用
孙沣楠,丛海勇,章豪,王云阁,徐刚..融合CTGAN与机器学习的温州市台风灾害损失评估方法研究[J].热带地理,2026,46(3):483-494,12.基金项目
温州市未来城市研究院开放基金项目(WL2023009) （WL2023009）
浙江省自然资源厅2024年度科技项目(2024ZJDZ036) （2024ZJDZ036）
浙江省教育厅科研项目(Y202456064) （Y202456064）
