一种基于生物启发的元启发算法的新方法,与随机森林相结合,以增强洪水易感性映射
Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, Soo-Mi Choi1
1Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
Journal of environmental management
|August 30, 2023
概括
这项研究使用机器学习和生物启发的算法增强了洪水易感性映射. 随机森林与侵袭性杂草优化模型被证明是最有效的预测易受洪水影响的地区.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 管理自然灾害的管理.
背景情况:
- 突发洪水带来重大风险,造成经济损失和死亡.
- 洪水易感性映射 (FSM) 是一个关键的非结构性洪水管理策略.
- 准确的FSM需要强大的预测模型,整合多种数据源.
研究的目的:
- 评估随机森林 (RF) 模型与侵入性杂草优化 (IWO),粘液模具算法 (SMA) 和色鸟优化 (SBO) 结合的性能.
- 为伊朗埃斯塔班市制定一个准确的FSM,确定易受洪水袭击的地区.
- 评估整合生物启发的算法与机器学习的有效性,以改善洪水预测.
主要方法:
- 集成 Sentinel-1 SAR 和 Landsat-8 光学卫星图像,以监测洪水范围.
- 开发一个数据集,包含509个洪水发生点和12个与洪水相关的标准 (地形,土地覆盖,气候).
- 应用持久方法 (70:30列车/测试分割) 和预处理技术 (CF,多线性,IGR) 进行模型优化.
- 用于FSM生成的RF,RF-IWO,RF-SBO和RF-SMA模型的比较分析.
主要成果:
- 射频-IWO模型表现出卓越的预测性能,实现了最低的RMSE (0.211培训,0.27测试) 和MAE (0.103培训,0.15测试),以及最高的R2 (0.821培训,0.707测试).
- 接收机操作特征 (ROC) 曲线分析显示,RF-IWO (0.983) 的曲线下面面积 (AUC) 是最高的,其次是RF-SBO (0.979),RF-SMA (0.963) 和RF (0.959).
- 该研究成功地为埃斯塔班地区生成了准确的洪水易感性地图.
结论:
- 将机器学习 (RF) 与生物启发的优化算法 (IWO,SMA,SBO) 结合起来,显著提高了洪水易感性绘图的准确性.
- 该RF-IWO模型对预测易受洪水影响的地区非常有效,为减少灾害风险提供了宝贵的工具.
- 这种新的方法为洪水管理和减灾策略提供了更精确,更可靠的方法.
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