刘悦忆,郑航,赵建世,万文华,谢观体

• 1:东莞理工学院生态环境与建筑工程学院
• 2:清华大学水利水电工程系
• 3:东莞市石鼓污水处理有限公司

摘要(Abstract)：

咸潮上溯是影响沿海地区水安全的重要环境问题。咸潮过程的有效模拟和精准预测是沿海河口地区咸潮防控与水安全保障面临的关键技术问题。针对传统数值模拟和数理统计预测方法在计算效率以及复杂条件下适用性不足的问题，采用深度学习方法建立了广东省东江河口咸潮上溯的预测模型，探讨了咸潮上溯对潮汐以及径流过程变化的响应特征。结果表明：综合物理过程统计指标和神经网络训练的深度学习方法，可以有效解析潮汐、径流以及河口含氯度之间复杂、动态的响应关系，东江河口万江站含氯度预测的决定系数达到0.8以上。极端干旱下东江下游博罗站的径流过程指标对于河口含氯度预测的准确度具有显著影响，是东江河口咸潮预测建模的关键变量。径流对河口咸潮的影响具有时间延迟，博罗站径流对河口含氯度影响的滞后时间为24～48 h。此外，感潮河段的流量往复幅度也对河口含氯度变化具有一定影响，可以作为咸潮预测的新指标。

关键词(KeyWords)： 咸潮上溯;深度学习;预测;径流过程;响应机制

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(51909035,52179009,U2040206)

作者(Author): 刘悦忆,郑航,赵建世,万文华,谢观体

DOI: 10.13928/j.cnki.wrahe.2022.10.010

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