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推进气候适应性洪水缓解:利用变压器-LSTM用于抽水站的水位预测
Pu-Yun Kow1, Jia-Yi Liou1, Ming-Ting Yang1
1Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan.
The Science of the total environment
|April 9, 2024
概括
一个新的变压器-LSTM模型提供了准确的洪水水位预测,改善了站管理和气候变化弹性. 这种人工智能方法提高了洪水预测,以减少灾害风险.
科学领域:
- 环境科学与工程环境科学与工程
- 人工智能在水资源管理中的应用
- 适应气候变化 适应气候变化
背景情况:
- 气候变化需要积极的洪水管理策略.
- 站的智能运行依赖于准确的水位预测.
- 人工智能 (AI) 为先进的洪水缓解提供了潜力.
研究的目的:
- 提出一种新的变压器-LSTM模型,用于准确的多步预测水位.
- 增强用于洪水管理的站智能运行.
- 分析影响洪水事件的相互联系的动态.
主要方法:
- 使用了集成变压器和LSTM模块的变压器-LSTM模型.
- 收集和处理了19647个基于10分钟的数据集 (2014-2020年) 用于培训,验证和测试.
- 评估模型性能与用于水位预测的基准模型进行比较.
主要成果:
- 变压器-LSTM模型在水位预测准确度方面明显优于基准模型.
- 在10分钟到60分钟的视界 (T+1到T+6) 实现可靠的预测.
- 证明有效增强输入因子连接和时间序列连接.
结论:
- 拟议的模型通过准确的水位预测来改善洪水管理.
- 了解相互关联的动态对于气候影响抵御能力和基础设施至关重要.
- 变压器-LSTM模型支持水务实践,弹性和减少灾害风险.
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