混合改进的时卷积网络模型在时间序列预测河水质量的应用
Yankun Hu1,2, Li Lyu1,2, Ning Wang3,4
1Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China.
Scientific reports
|July 12, 2023
概括
准确的河流水质预测对于环境保护至关重要. 这项研究引入了一种新的混合深度神经网络模型,结合了降噪,时间序列分解和先进的神经网络技术,以提高预测水质变化的准确性.
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:
- 河流水质量监测对于环境保护至关重要.
- 传统的时间序列预测方法与水质数据固有的复杂周期性,季节性和非线性作斗争.
- 需要准确的预测模型来有效管理和保护河流生态系统.
研究的目的:
- 提出一种新的混合深度神经网络模型,用于改进河水质量预测.
- 解决噪音,季节性和非线性在时间序列水质数据中所带来的挑战.
- 提高河水质量预测的准确性和可靠性.
主要方法:
- 一种混合深度神经网络模型,集成了萨维塔基-戈莱 (SG) 过器,STL时间序列分解,自我注意力机制和时间卷积网络 (TCN).
- 用于减少河水质量时间序列数据中的噪音的SG过器.
- STL分解将数据分成趋势,季节和残余组件,由Self-attention和TCN分别处理趋势和残余序列.
主要成果:
- 与现有模型相比,拟议的混合模型在河水质量预测方面表现优越.
- 使用开源和私人水质数据集的实验验证证证了该模型的有效性.
- 该方法在两条不同的河流的水质数据中取得了最佳的预测结果.
结论:
- 开发的混合深度神经网络模型有效地处理河水质量时间序列数据的复杂性.
- 这种方法显著提高了水质预测的准确性,有助于环境保护工作.
- 该模型在不同数据集中的强大性能突显了其在水资源管理中的实际应用潜力.
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