一种基于变化模式分解和双向封闭递归单元的混合水质预测模型
Jiange Jiao1, Qianqian Ma2, Senjun Huang3
1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China; Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, Hangzhou, China.
概括
这项研究引入了一种混合模型,用于准确预测水质,增强河流生态管理. 这种新的方法提高了溶解氧 (DO) 和 (pH) 潜力的预测准确度.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水文学的水文学
背景情况:
- 准确的水质预测对于有效的河流生态管理和污染预防至关重要.
- 水质数据的非线性和不稳定性对传统预测方法构成重大挑战.
研究的目的:
- 开发一种混合模型,以提高水质预测准确度.
- 为应对非线性和不稳定的水质数据所带来的挑战.
主要方法:
- 提出了一个混合模型,结合了Sparrow搜索算法优化的变化模式分解 (SSA-VMD) 和双向门式循环单元 (BiGRU).
- 小搜索算法 (SSA) 使用模糊 (FE) 作为适应性函数优化了VMD参数.
- SSA-VMD将水质数据分解为组件,然后使用BiGRU单独预测这些组件.
主要成果:
- 与基于EMD和CEEMDAN的模型等现有方法相比,提出的SSA-VMD-BiGRU模型显示出更高的预测准确性和稳定性.
- 在验证试验中,溶解氧 (DO) 的预测准确率达到97.8%, (pH) 的潜力达到96.1%.
- 该模型使用来自中国安湖的现实世界DO和pH数据进行了验证.
结论:
- 开发的混合模型为预测水质参数 (如DO和pH) 提供了强大的解决方案.
- 这种方法为保护河水质量和防止污染的战略提供了宝贵的技术支持.
- 这些发现突显了SSA-VMD-BiGRU在复杂环境数据分析和预测方面的潜力.
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