基于BiTCN-GRU建模的城市淹水的深度预测
Quan Wang1,2, Mingjie Tang2, Pei Shi1,2
1School of Internet of Things Engineering, Wuxi University, Jiangsu, China.
PloS one
|April 23, 2025
概括
这项研究引入了一种新的混合深度学习模型,BiTCN-GRU,用于准确的城市淹水深度预测. 该模型通过在预测大雨影响方面超越现有方法来改善灾害预防.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 人工智能的人工智能
背景情况:
- 由于城市化迅速和极端天气,城市淹水在中国是一个越来越大的挑战.
- 现有的水文模型在数据要求,复杂性和预测准确性方面扎.
- 准确的浸水深度预测对于预防和减轻灾害至关重要.
研究的目的:
- 开发一个更准确,更可通用的模型来预测城市水浸深度.
- 解决数据密集型和复杂的城市环境中传统水文模型的局限性.
主要方法:
- 提出了一种混合深度学习模型:BiTCN-GRU,集成双向时间卷积网络 (BiTCN) 和门式循环单元 (GRU).
- BiTCN通过前进/后退卷曲捕获降雨和水浸深度特征,将它们输入GRU进行预测.
- 利用了明山路和河路的数据集进行模型评估.
主要成果:
- BiTCN-GRU模型实现了高精度,MAE,RMSE和R2值为1.56,3.62和88.31% (明山路),以及3.44,8.08和92.64% (海路).
- 与GBDT,LSTM和TCN-LSTM模型相比,表现出优越的性能.
- 证实了该模型在短期淹水预测中的有效性.
结论:
- 该BiTCN-GRU模型提供了一个强大的和准确的解决方案,用于城市淹水深度预测.
- 提供了有价值的科学见解和对城市水资源管理和灾害减缓战略的理论支持.
- 强调混合深度学习方法在应对复杂的环境挑战方面的潜力.
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