基于矩阵场景的城市洪水损害预测通过卷积神经网络
Haojun Yuan1, Mo Wang1, Jianjun Li1
1College of Architecture and Urban Planning, Guangzhou University, Guangzhou, 510006, China.
Journal of environmental management
|November 4, 2024
概括
本研究介绍了一种使用卷积神经网络的新工具,用于准确评估城市雨水洪水危险. 它确定了脆弱地区并估计了经济损失,提高了气候适应力和城市规划.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 人工智能的人工智能
背景情况:
- 传统的洪水评估方法与城市异质性和复杂的气候场景作斗争.
- 城市暴雨洪水对基础设施和经济构成重大风险.
- 准确的危险评估对于有效的城市规划和气候适应性至关重要.
研究的目的:
- 通过卷积神经网络引入一个高分辨率的工具,用于评估城市雨水洪水的危险性.
- 考虑城市空间异质性和复杂的气候变化影响.
- 提高洪水风险预测的准确性和可解释性.
主要方法:
- 开发一个卷积神经网络 (CNN) 模型用于洪水危险评估.
- 在中国深的案例研究申请.
- 性能分析比较预测和实际洪水深度,重点关注极端事件.
主要成果:
- 美国有线电视新闻网模型在预测大多数城市洪水深度方面表现出很高的准确性,但在极端洪水预测 (>35厘米) 中存在局限性.
- 西部和深中部,建筑业繁荣的地区被确定为高度脆弱的地区.
- 据估计,在严重的25号情景下,经济损失达到约254.84亿美元,商业和住宅地区受到影响最大.
结论:
- 调查结果强调了气候变化情景下洪水影响的升级,需要加强缓解策略.
- 防洪应优先考虑减少淹水面积和整合土地使用特征.
- 该工具有助于精确识别易受洪水影响的地区,加强疏散计划,城市可持续性和气候适应力.
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