图形尖端神经网络用于先进的城市洪水风险评估
Zhantu Liang1, Xuhong Fang2, Zhanhao Liang3
1Department of Artificial Intelligence and Data Science, Guangzhou Xinhua University, Dongguan 523133, Guangdong, China.
iScience
|November 11, 2024
概括
一个新的图形尖端神经网络 (SNN) 模型整合了各种数据,用于优异的城市洪水风险预测. 这种方法提高了可扩展的,实时的洪水管理的精度和能源效率.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 城市规划 城市规划
背景情况:
- 城市洪水对城市规划和居民安全构成重大风险.
- 传统的洪水风险模型难以满足数据需求和可扩展性.
- 现有的方法往往无法有效地整合多样化的数据源.
研究的目的:
- 开发一个先进的城市洪水风险评估模型.
- 克服传统物理和数据驱动的洪水模型的局限性.
- 增强从异质数据中提取空间和时间特征.
主要方法:
- 开发了一个混合模型,将图形卷积网络 (GCN) 和尖端神经网络 (SNN) 结合起来.
- 通过整合来自中国六个城市的社交媒体,天气和地理数据,创建了一个全面的洪水风险数据集.
- 评估模型性能与GCN和长短期记忆 (LSTM) 模型相比.
主要成果:
- 拟议的Graph SNN模型实现了高精度 (85.3%),精度 (0.811),回忆 (0.832) 和F1得分 (0.821).
- 与GCN和LSTM模型相比,在洪水风险预测方面表现优越.
- 显著提高了能源效率,表明可用于实时应用的可扩展性.
结论:
- 图形SNN模型为城市洪水风险评估提供了有效和可持续的解决方案.
- 有效地处理异质数据,减少能源消耗,以便实时预测洪水.
- 通过提高准确性,可扩展性和能源效率,推进城市洪水管理.
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