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在气候变化场景下,使用PSO-ML合预测洪水易发生地区的时空变化
Azhar Ali Laghari1, Yongheng Shen1, Akash Kumar2
1College of Resources and Environment, Shanxi Agricultural University, Jinzhong, 030801, Shanxi Province, China.
Scientific reports
|December 2, 2025
概括
气候变化正在增加极端降雨天数,导致洪水易发生地区向南转移. 先进的机器学习模型可以准确预测这些变化,帮助制定有效的洪水风险管理策略.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 地理空间分析的研究.
背景情况:
- 气候变化需要改善洪水灾害预测.
- 传统的洪水风险评估方法缺乏准确性.
- 了解时空洪水动态对于减轻灾害至关重要.
研究的目的:
- 开发精确的机器学习增强模型,用于洪水灾害风险评估.
- 在各种气候场景下分析时间和空间洪水特征.
- 确定影响中国山西省洪水风险的关键因素.
主要方法:
- 集成粒子优化-机器学习 (PSO-ML) 模型与通用循环模型 (GCM) 数据.
- 应用了PSO-XGBoost,PSO-RF和PSO-KNN,以提高预测的准确性.
- 分析了土地使用变化,海拔和坡度作为影响因素.
主要成果:
- 从1981年到2021年,极端降雨天数显著增加.
- 在极端降雨天观察到北向南的梯度.
- PSO-ML模型实现了高预测准确性 (AUC高达0.98).
- 土地使用,海拔和坡度是洪水风险的主要驱动因素.
- 预计易受洪水影响的地区将向南移动,在SSP370和SSP585情景下大幅增加.
结论:
- 与传统方法相比,PSO-ML模型提供了优越的洪水风险预测.
- 气候变化正在加剧洪水风险,特别是在南部地区.
- 该研究为区域洪水风险管理和减少灾害提供了关键的见解.
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