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使用MLP进行基于深度学习的河流流量预测:对泰戈河和蒙德戈河应用的比较探索性分析
Gonçalo Jesus1, Zahra Mardani1, Elsa Alves1
1Laboratório Nacional de Engenharia Civil, Avenida do Brasil 101, 1700-066 Lisboa, Portugal.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了使用多层感知器 (MLP) 模型进行人工智能驱动的河流流量预测. 这些模型提供了准确的三天河流排放预测,增强了水资源管理和洪水减缓工作.
科学领域:
- 水文学的水文学
- 人工智能的人工智能
- 水资源管理 水资源管理
背景情况:
- 准确的河流流量预测对于有效的水资源管理,洪水控制和基础设施运营至关重要.
- 现有的预测方法对短期预测的准确性和及时性可能有局限性.
- 控制大的河流需要可靠的预测,以实现运营效率和下游影响减轻.
研究的目的:
- 开发和演示用于短期河流流量预测的创新远程服务.
- 创建和增强人工智能 (AI) 模型,特别是多层感知器 (MLP),用于预测河流排放.
- 为未来3天提供精确和及时的河流流量预测.
主要方法:
- 利用多层感知器 (MLP) 架构用于河流排放预测.
- 使用来自葡萄牙国家水资源信息系统 (SNIRH) 的综合水文数据.
- 在泰戈河和蒙德戈河流域对MLP模型性能进行了比较研究,详细介绍了数据准备,模型培训和预测.
主要成果:
- 在选定的案例研究中,MLP模型在短期河流流量预测中显示了可接受的准确性.
- 这些模型有效地捕获了泰戈河和蒙德戈河的排放模式和峰值事件.
- 对比分析证实了模型在不同水文场景中的表现.
结论:
- 多项计划模型提供了一种可行的数据驱动方法,用于提高短期河流流量预测的准确性.
- 开发的AI服务可以显著改善水资源管理,决策和洪水减缓策略.
- 这些预报为下游水文和气象预报系统提供了有价值的边界条件.
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