沿海城市的水位预测使用混合深度学习方法
Abdur Rahman1, M Hafidz Omar1, Tahir Mahmood2
1Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
The Science of the total environment
|October 16, 2025
概括
一个新的混合深度学习模型准确地预测了威尼斯等沿海城市的每小时水位. 这种先进的洪水预测系统提供了卓越的准确性和稳定性,即使数据有限.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 人工智能和机器学习
- 适应气候变化 适应气候变化
背景情况:
- 沿海城市在实时洪水预测方面面临重大挑战,原因是复杂的水文动态和稀缺的历史数据.
- 准确的预测对于开发有效的洪水预警系统至关重要,特别是在气候敏感地区.
研究的目的:
- 引入和评估一种新的混合深度学习模型,用于准确的每小时水位预测,在意大利威尼斯.
- 在各种数据条件下,与已建立的基线方法对比,评估模型的性能.
主要方法:
- 开发一种混合深度学习模型,CNN-Transformer-SKANs,集成卷积神经网络 (CNNs),变压器层和 Kolmogorov Arnold 网络 (SKANs).
- 在两年的高分辨率气象和水文数据上训练模型,包括风速,潮水位,湿度,大气压力和先前的水位.
- 对LSTM,CNN-LSTM和变压器变体等基线模型进行比较分析.
主要成果:
- CNN-变压器-SKANs模型实现了卓越的准确性,纳什-萨特克利夫效率 (NSE) 约为0.99,根平均平方误差 (RMSE) 低于0.03米.
- 该模型在减少训练数据场景和合成极端值模拟下显示出稳定性,保持高精度 (RMSE 0.02-0.03 m,NSE高达 0.99).
- 超过了传统的LSTM和CNN-LSTM基线,其RMSEs为0.04-0.07m和NSEs为0.90-0.97.
结论:
- 拟议的CNN-Transformer-SKANs模型为沿海城市环境中的洪水预警系统提供了可靠和高度准确的解决方案.
- 该模型的强大泛化性能使其适用于气候敏感地区,需要先进的洪水预测能力.
- 混合深度学习架构显示出解决复杂的水文预测挑战的重大前景.
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