利用基于GIS的AHP,遥感和机器学习来评估巴基斯坦佩沙瓦不同类型洪水的易感性
Muhammad Tayyab1, Muhammad Hussain1, Jiquan Zhang2
1Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China.
Journal of environmental management
|November 3, 2024
概括
巴基斯坦 巴基斯坦 巴基斯坦.
科学领域:
- 环境科学,地理空间分析,机器学习
背景情况:
- 巴基斯坦多样化的地形导致各种年度洪水类型,造成重大破坏.
- 地区对不同类型 (河流,城市,突发) 洪水的敏感性在很大程度上尚未评估.
研究的目的:
- 在巴基斯坦佩沙瓦评估多种类型的洪水易感性,使用集成的GIS,遥感和机器学习方法.
- 为了比较机器学习模型对洪水易感性映射的性能.
主要方法:
- 采用基于GIS的分析层次流程 (AHP) 进行初始易感性评估.
- 利用随机森林 (RF) 和极端梯度提升 (XG-Boost) 机器学习算法进行多种类型的洪水易感性分析.
- 使用相关系数 (R2),平均绝对误差 (MAE),根平均平方误差 (RMSE) 和曲线下的面积 (AUC) 评估模型性能.
主要成果:
- XG-Boost模型表现出优于RF和AHP的性能,具有更高的R2和AUC值和更低的MAE和RMSE.
- 洪水易感性地图显示,该地区20.9%的地区容易受到河流洪水,30.27%的城市洪水,48.68%的城市洪水.
- XG-Boost 的 AUC 值为 0.87,超过了 AHP (0.70) 和 RF (0.86).
结论:
- 综合的GIS,遥感和XG-Boost方法有效评估多种类型的洪水易感性.
- 调查结果为佩沙瓦的洪水风险管理和可持续土地利用规划提供了关键数据.
- 该研究强调了根据特定的洪水类型和区域易感性制定量身定制的洪水管理策略的关键需求.
相关概念视频
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