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预测和缓解数据稀缺的水的总溶解气体超和的概括方法
Shicheng Li1, Junfeng Chen1, Yuanming Wang2
1Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden.
Water research
|January 10, 2026
概括
一个新的框架,TransTDG,准确预测总溶解气 (TDG) 下游的水使用转移学习和符号回归. 这种可解释的模型有助于在数据稀缺的环境中进行生态管理.
科学领域:
- 环境科学 环境科学
- 水力发电工程 水力发电工程
- 计算生态学计算生态学
背景情况:
- 总溶解气体 (TDG) 在水下游的超和造成了生态风险,特别是对鱼类,因为气泡病.
- 现有的 TDG 预测模型往往受特定地点,高数据要求和缺乏可解释性限制.
研究的目的:
- 开发TransTDG,一个通用,可解释和可转移的框架,用于在数据稀缺的水环境中进行TDG预测和缓解.
- 为了弥合站点依赖的黑子模型和复杂的基于物理的模拟用于TDG管理之间的差距.
主要方法:
- 将转移学习 (TL) 与符号回归结合起来,创建一个可适应和明确的 TDG 建模框架.
- 引入两种TL策略,以提高模型适应性,以适应各种水条件.
- 在哥伦比亚河和蛇河下游的12个数据有限的水上验证模型.
主要成果:
- 平均R2为0.93和RMSE为1.74%,只有1%的本地数据.
- 即使没有上游TDG测量,也保持了高精度 (R2 = 0.80,RMSE = 2.98%),平均相对误差为1.0%.
- 通过流量优化证明了运行相关性,产生了4.7%的TDG减少和8.5%的功率输出增加.
结论:
- TransTDG提供了一个可转移和透明的解决方案,用于在数据受限制的环境中进行TDG建模,从而推进生态管理.
- 该框架在各种河流系统中被证明是有效的,超过95%的预测显示在额外的测试水上相对误差为<5%.
- 这种方法支持可持续的水力发电运营,通过实现有效的TDG缓解和改进能源发电.
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