在洪水预测中,从数据到行动,利用图形神经网络和数字双胞胎可视化来实现洪水预测
Naghmeh Shafiee Roudbari1, Shubham Rajeev Punekar2, Zachary Patterson3
1Immersive and Creative Technologies Lab, Department Computer Science and Software Engineering, Concordia University, Montreal, Canada. naghmeh.shafiee@concordia.ca.
Scientific reports
|August 10, 2024
概括
本研究介绍了LocalFLoodNet,这是一个用于高级洪水预测和水位预测的图形神经网络. 一个模拟原型通过视觉洞察力帮助预防灾害和制定政策.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境科学中的人工智能
- 地理空间分析和灾害管理
背景情况:
- 水文系统表现出复杂的非线性动态,使得准确的洪水预测具有挑战性.
- 现有的洪水预测方法往往忽视了空间互连,并且缺乏可靠的可视化工具来评估水位.
- 有效的洪水管理需要先进的预测模型和决策支持系统.
研究的目的:
- 开发一个先进的图形神经网络模型 (LocalFLoodNet) 以提高水位预测.
- 创建一个模拟原型,以可视化洪水影响,并支持灾害预防和政策制定.
- 为评估洪水影响和评估城市地区预防策略提供一个全面的框架.
主要方法:
- 使用图形学习模块实现图形神经网络 (LocalFLoodNet),以建模水文网络连接.
- 开发一个使用数字双胞胎的交互式模拟原型,用于场景分析和预测水位的可视化.
- 在大蒙特利尔地区 (GMA) 应用和验证模型和原型,特别是北克省的特雷.
主要成果:
- LocalFLoodNet有效地捕获水系统内的互连,以改善水位预测.
- 模拟原型为洪水风险管理中的决策提供了有价值的视觉见解.
- 该研究展示了对洪水影响评估和预防措施评估的综合方法.
结论:
- 图形神经网络和模拟原型的集成在洪水预测和水资源管理方面取得了重大进展.
- 开发的工具提高了预测水位的能力,并评估了洪水减缓策略的有效性.
- 这项研究为应用先进的人工智能和数字双胞胎技术应对不同地理区域的水文挑战奠定了基准.
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