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气候变化对未来洪水易感性预测的影响在南亚的共同社会经济路径场景下,使用人工智能算法
Saeid Janizadeh1, Dongkyun Kim2, Changhyun Jun3
1Department of Civil, Environmental and Construction Engineering, and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA.
Journal of environmental management
|July 9, 2024
概括
气候变化对南亚的洪水易感性产生重大影响. 人工智能模型预测风险增加,特别是在高排放场景下,强调了适应性洪水风险管理的必要性.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 气候变化对南亚构成越来越大的威胁,南亚是一个极易受到自然灾害影响的地区.
- 了解未来的洪水易感性对于区域适应和减缓战略至关重要.
研究的目的:
- 调查气候变化对六个南亚国家的洪水易感性的影响.
- 为了比较三个人工智能 (AI) 算法在不同气候场景下预测洪水易感性的性能.
- 确定影响该地区洪水易感性的关键气候变量.
主要方法:
- 利用了三个AI算法:K-最近邻居 (KNN),条件推断随机森林 (CIRF) 和规范随机森林 (RRF).
- 采用了来自2452个历史洪水事件和气候变量的月度,季节和年度时间框架的数据.
- 使用接收器运行特征曲线 (AUC) 下面面积的尺度,评估模型性能.
主要成果:
- 规范的随机森林 (RRF) 模型显示了最高的准确性 (AUC=0.94),超过了CIRF (AUC=0.91) 和KNN (AUC=0.86).
- 在最热季度的降雨量,年度降雨量和最潮湿月份的降雨量被确定为洪水易感性的关键预测指标.
- 洪水易感性地图显示,在不同的共享社会经济路径 (SSP) 场景和未来时期下,洪水易感性地图显示出显著的变化,高风险地区显著增加.
结论:
- 人工智能模型,特别是RRF,是评估南亚气候变化引起的洪水易感性的有效工具.
- 气候变化场景显著改变洪水风险分布,需要积极的洪水风险管理策略.
- 该研究为决策者和利益相关者提供了关键的见解,帮助他们制定南亚的气候适应性基础设施和适应计划.
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