河流洪水的管理和预测利用人工智能进化算法的优化方法
Rana Muhammad Adnan Ikram1,2,3, Mo Wang2, Hossein Moayedi4,5
1Water Science and Environmental Research Centre, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, 518060, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了进化的人工智能算法,用于增强洪水易感性映射. 混合型模型显著提高了洪水预测准确度,并优化了城市地区的管理策略.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 人工智能的人工智能
背景情况:
- 洪水对全球构成重大风险,需要有效的洪水易感性测绘 (FSM),特别是在城市环境中.
- 传统的洪水预测模型与数据复杂性和计算需求作斗争.
- 人工智能,特别是进化算法,为更强大,更适应的洪水预测提供了潜力.
研究的目的:
- 评估人工神经网络 (ANN) 算法与FSM的进化计算相集成.
- 为了提高洪水预测的准确性和优化洪水管理策略,使用新的AI方法.
- 评估黑洞算法 (BHA),未来搜索算法 (FSA),基于堆的优化 (HBO) 和多元优化 (MVO) 对FSM的性能.
主要方法:
- 使用ANN模型训练了八个影响洪水的关键地理因素:海拔,降雨量,斜率,NDVI,外观,地质,土地使用和河流数据.
- 采用进化算法 (BHA,FSA,HBO,MVO) 来优化洪水预测的ANN性能.
- 使用法尔斯地区历史洪水数据验证模型,采用诸如平均平方误差 (MSE),平均绝对误差 (MAE) 和接收器操作特征 (ROC) 曲线等指标.
主要成果:
- 混合型模型 (BHA-MLP,FSA-MLP,MVO-MLP,HBO-MLP) 在预测准确度方面取得了显著的改进.
- 在测试的进化算法中观察到精度指数和曲线下面积 (AUC) 值的显著增加.
- 该研究证实了进化AI在增强FSM和洪水管理方面的有效性.
结论:
- 混合进化AI模型为城市洪水脆弱性绘制提供了有效和成本效益的解决方案.
- 这些先进的模型为改善防洪准备和应急响应计划提供了宝贵的见解.
- 进化算法的集成代表了人工智能驱动的地理空间风险评估的重大进步.
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