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通过MLA-Mamba混合神经网络与GRPO优化进行地表水质量预测
Ronghao Wei1, Hang Chen1, Haihe Wang2,3
1School of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
Scientific reports
|January 20, 2026
概括
一个新的MLA-Mamba深度学习模型通过捕捉复杂的时间和空间动态来改善地表水质预测. 这种方法提高了污染的早期预警和水资源管理.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水资源管理 水资源管理
背景情况:
- 地表水质量预测对于污染控制和可持续的水资源管理至关重要.
- 精确的预测受到复杂的非线性时空动态和变量间关系的阻碍.
- 传统方法往往无法捕捉到这些复杂的模式,限制了预测准确度.
研究的目的:
- 引入一种新的混合深度学习框架,MLA-Mamba,用于增强地表水质量预测.
- 整合Mamba用于时间依赖,多头本地注意力 (MLA) 用于空间相关性,以及梯度重构优化 (GRPO) 用于稳定训练.
- 通过多任务学习,共同预测多个水质指标 (CODMn,NH3-N,TP,TN).
主要方法:
- 开发了MLA-Mamba框架,结合了Mamba序列建模和MLA用于空间特征提取.
- 应用渐变重组化优化 (GRPO) 适应性学习速度调整和改进模型稳定性.
- 利用多任务学习同时预测CODMn,NH3-N,TP和TN,利用变量之间的依赖关系.
- 量化预测不确定性使用蒙特卡洛脱落值进行置信区间估计.
主要成果:
- 在现实数据集上,MLA-Mamba模型表现出与基线方法相比的一致的性能改进.
- 该框架有效地捕捉了长距离的时间依赖性和局部的空间相关性.
- 在培训期间,GRPO战略加快了融合,提高了模型稳定性.
- 量化不确定性为风险意识评估提供了有价值的信心区间.
结论:
- MLA-Mamba框架为地表水质量预测提供了强大而有效的解决方案.
- 集成先进的序列建模,注意力机制和自适应优化显著提高预测准确性.
- 该方法支持通过可靠的预测改善污染早期预警和可持续的水资源管理.
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